Discrete Semantic Matrix Factorization Hashing for Cross-Modal Retrieval

Jianyang Qin, Lunke Fei, Shaohua Teng, Wei Zhang, Genping Zhao, Haoliang Yuan

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Auto-TLDR; Discrete Semantic Matrix Factorization Hashing for Cross-Modal Retrieval

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Hashing has been widely studied for cross-modal retrieval due to its promising efficiency and effectiveness in massive data analysis. However, most existing supervised hashing has the limitations of inefficiency for very large-scale search and intractable discrete constraint for hash codes learning. In this paper, we propose a new supervised hashing method, namely, Discrete Semantic Matrix Factorization Hashing (DSMFH), for cross-modal retrieval. First, we conduct the matrix factorization via directly utilizing the available label information to obtain a latent representation, so that both the inter-modality and intra-modality similarities are well preserved. Then, we simultaneously learn the discriminative hash codes and corresponding hash functions by deriving the matrix factorization into a discrete optimization. Finally, we adopt an alternatively iterative procedure to efficiently optimize the matrix factorization and discrete learning. Extensive experimental results on three widely used image-tag databases demonstrate the superiority of the DSMFH over state-of-the-art cross-modal hashing methods.

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Fast Discrete Cross-Modal Hashing Based on Label Relaxation and Matrix Factorization

Donglin Zhang, Xiaojun Wu, Zhen Liu, Jun Yu, Josef Kittler

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Auto-TLDR; LRMF: Label Relaxation and Discrete Matrix Factorization for Cross-Modal Retrieval

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In recent years, cross-media retrieval has drawn considerable attention due to the exponential growth of multimedia data. Many hashing approaches have been proposed for the cross-media search task. However, there are still open problems that warrant investigation. For example, most existing supervised hashing approaches employ a binary label matrix, which achieves small margins between wrong labels (0) and true labels (1). This may affect the retrieval performance by generating many false negatives and false positives. In addition, some methods adopt a relaxation scheme to solve the binary constraints, which may cause large quantization errors. There are also some discrete hashing methods that have been presented, but most of them are time-consuming. To conquer these problems, we present a label relaxation and discrete matrix factorization method (LRMF) for cross-modal retrieval. It offers a number of innovations. First of all, the proposed approach employs a novel label relaxation scheme to control the margins adaptively, which has the benefit of reducing the quantization error. Second, by virtue of the proposed discrete matrix factorization method designed to learn the binary codes, large quantization errors caused by relaxation can be avoided. The experimental results obtained on two widely-used databases demonstrate that LRMF outperforms state-of-the-art cross-media methods.

Object Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing

Qianqian Zhang, Yazhou Liu, Quansen Sun

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Auto-TLDR; Optimized Projection Supervised Discrete Hashing for Large-Scale Remote Sensing Image Object Classification

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Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has been widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications

Cross-Media Hash Retrieval Using Multi-head Attention Network

Zhixin Li, Feng Ling, Chuansheng Xu, Canlong Zhang, Huifang Ma

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Auto-TLDR; Unsupervised Cross-Media Hash Retrieval Using Multi-Head Attention Network

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The cross-media hash retrieval method is to encode multimedia data into a common binary hash space, which can effectively measure the correlation between samples from different modalities. In order to further improve the retrieval accuracy, this paper proposes an unsupervised cross-media hash retrieval method based on multi-head attention network. First of all, we use a multi-head attention network to make better matching images and texts, which contains rich semantic information. At the same time, an auxiliary similarity matrix is constructed to integrate the original neighborhood information from different modalities. Therefore, this method can capture the potential correlations between different modalities and within the same modality, so as to make up for the differences between different modalities and within the same modality. Secondly, the method is unsupervised and does not require additional semantic labels, so it has the potential to achieve large-scale cross-media retrieval. In addition, batch normalization and replacement hash code generation functions are adopted to optimize the model, and two loss functions are designed, which make the performance of this method exceed many supervised deep cross-media hash methods. Experiments on three datasets show that the average performance of this method is about 5 to 6 percentage points higher than the state-of-the-art unsupervised method, which proves the effectiveness and superiority of this method.

Hierarchical Deep Hashing for Fast Large Scale Image Retrieval

Yongfei Zhang, Cheng Peng, Zhang Jingtao, Xianglong Liu, Shiliang Pu, Changhuai Chen

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Auto-TLDR; Hierarchical indexed deep hashing for fast large scale image retrieval

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Fast image retrieval is of great importance in many computer vision tasks and especially practical applications. Deep hashing, the state-of-the-art fast image retrieval scheme, introduces deep learning to learn the hash functions and generate binary hash codes, and outperforms the other image retrieval methods in terms of accuracy. However, all the existing deep hashing methods could only generate one level hash codes and require a linear traversal of all the hash codes to figure out the closest one when a new query arrives, which is very time-consuming and even intractable for large scale applications. In this work, we propose a Hierarchical Deep HASHing(HDHash) scheme to speed up the state-of-the-art deep hashing methods. More specifically, hierarchical deep hash codes of multiple levels can be generated and indexed with tree structures rather than linear ones, and pruning irrelevant branches can sharply decrease the retrieval time. To our best knowledge, this is the first work to introduce hierarchical indexed deep hashing for fast large scale image retrieval. Extensive experimental results on three benchmark datasets demonstrate that the proposed HDHash scheme achieves better or comparable accuracy with significantly improved efficiency and reduced memory as compared to state-of-the-art fast image retrieval schemes.

VSB^2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing

Xin Li, Xiangfeng Wang, Bo Jin, Wenjie Zhang, Jun Wang, Hongyuan Zha

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Auto-TLDR; VSB^2-Net: inductive zero-shot hashing for image retrieval

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Zero-shot hashing aims at learning hashing model from seen classes and the obtained model is capable of generalizing to unseen classes for image retrieval. Inspired by zero-shot learning, existing zero-shot hashing methods usually transfer the supervised knowledge from seen to unseen classes, by embedding the hamming space to a shared semantic space. However, this makes instances difficult to distinguish due to limited hashing bit numbers, especially for semantically similar unseen classes. We propose a novel inductive zero-shot hashing framework, i.e., VSB^2-Net, where both semantic space and visual feature space are embedded to the same hamming space instead. The reconstructive semantic relationships are established in the hamming space, preserving local similarity relationships and explicitly enlarging the discrepancy between semantic hamming vectors. A two-task architecture, comprising of classification module and visual feature reconstruction module, is employed to enhance the generalization and transfer abilities. Extensive evaluation results on several benchmark datasets demonstratethe superiority of our proposed method compared to several state-of-the-art baselines.

Label Self-Adaption Hashing for Image Retrieval

Jianglin Lu, Zhihui Lai, Hailing Wang, Jie Zhou

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Auto-TLDR; Label Self-Adaption Hashing for Large-Scale Image Retrieval

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Hashing has attracted widespread attention in image retrieval because of its fast retrieval speed and low storage cost. Compared with supervised methods, unsupervised hashing methods are more reasonable and suitable for large-scale image retrieval since it is always difficult and expensive to collect true labels of the massive data. Without label information, however, unsupervised hashing methods can not guarantee the quality of learned binary codes. To resolve this dilemma, this paper proposes a novel unsupervised hashing method called Label Self-Adaption Hashing (LSAH), which contains effective hashing function learning part and self-adaption label generation part. In the first part, we utilize anchor graph to keep the local structure of the data and introduce joint sparsity into the model to extract effective features for high-quality binary code learning. In the second part, a self-adaptive cluster label matrix is learned from the data under the assumption that the nearest neighbor points should have a large probability to be in the same cluster. Therefore, the proposed LSAH can make full use of the potential discriminative information of the data to guide the learning of binary code. It is worth noting that LSAH can learn effective binary codes, hashing function and cluster labels simultaneously in a unified optimization framework. To solve the resulting optimization problem, an Augmented Lagrange Multiplier based iterative algorithm is elaborately designed. Extensive experiments on three large-scale data sets indicate the promising performance of the proposed LSAH.

Improved Deep Classwise Hashing with Centers Similarity Learning for Image Retrieval

Ming Zhang, Hong Yan

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Auto-TLDR; Deep Classwise Hashing for Image Retrieval Using Center Similarity Learning

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Deep supervised hashing for image retrieval has attracted researchers' attention due to its high efficiency and superior retrieval performance. Most existing deep supervised hashing works, which are based on pairwise/triplet labels, suffer from the expensive computational cost and insufficient utilization of the semantics information. Recently, deep classwise hashing introduced a classwise loss supervised by class labels information alternatively; however, we find it still has its drawback. In this paper, we propose an improved deep classwise hashing, which enables hashing learning and class centers learning simultaneously. Specifically, we design a two-step strategy on center similarity learning. It interacts with the classwise loss to attract the class center to concentrate on the intra-class samples while pushing other class centers as far as possible. The centers similarity learning contributes to generating more compact and discriminative hashing codes. We conduct experiments on three benchmark datasets. It shows that the proposed method effectively surpasses the original method and outperforms state-of-the-art baselines under various commonly-used evaluation metrics for image retrieval.

Joint Learning Multiple Curvature Descriptor for 3D Palmprint Recognition

Lunke Fei, Bob Zhang, Jie Wen, Chunwei Tian, Peng Liu, Shuping Zhao

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Auto-TLDR; Joint Feature Learning for 3D palmprint recognition using curvature data vectors

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3D palmprint-based biometric recognition has drawn growing research attention due to its several merits over 2D counterpart such as robust structural measurement of a palm surface and high anti-counterfeiting capability. However, most existing 3D palmprint descriptors are hand-crafted that usually extract stationary features from 3D palmprint images. In this paper, we propose a feature learning method to jointly learn compact curvature feature descriptor for 3D palmprint recognition. We first form multiple curvature data vectors to completely sample the intrinsic curvature information of 3D palmprint images. Then, we jointly learn a feature projection function that project curvature data vectors into binary feature codes, which have the maximum inter-class variances and minimum intra-class distance so that they are discriminative. Moreover, we learn the collaborative binary representation of the multiple curvature feature codes by minimizing the information loss between the final representation and the multiple curvature features, so that the proposed method is more compact in feature representation and efficient in matching. Experimental results on the baseline 3D palmprint database demonstrate the superiority of the proposed method in terms of recognition performance in comparison with state-of-the-art 3D palmprint descriptors.

DFH-GAN: A Deep Face Hashing with Generative Adversarial Network

Bo Xiao, Lanxiang Zhou, Yifei Wang, Qiangfang Xu

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Auto-TLDR; Deep Face Hashing with GAN for Face Image Retrieval

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Face Image retrieval is one of the key research directions in computer vision field. Thanks to the rapid development of deep neural network in recent years, deep hashing has achieved good performance in the field of image retrieval. But for large-scale face image retrieval, the performance needs to be further improved. In this paper, we propose Deep Face Hashing with GAN (DFH-GAN), a novel deep hashing method for face image retrieval, which mainly consists of three components: a generator network for generating synthesized images, a discriminator network with a shared CNN to learn multi-domain face feature, and a hash encoding network to generate compact binary hash codes. The generator network is used to perform data augmentation so that the model could learn from both real images and diverse synthesized images. We adopt a two-stage training strategy. In the first stage, the GAN is trained to generate fake images, while in the second stage, to make the network convergence faster. The model inherits the trained shared CNN of discriminator to train the DFH model by using many different supervised loss functions not only in the last layer but also in the middle layer of the network. Extensive experiments on two widely used datasets demonstrate that DFH-GAN can generate high-quality binary hash codes and exceed the performance of the state-of-the-art model greatly.

Cross-spectrum Face Recognition Using Subspace Projection Hashing

Hanrui Wang, Xingbo Dong, Jin Zhe, Jean-Luc Dugelay, Massimo Tistarelli

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Auto-TLDR; Subspace Projection Hashing for Cross-Spectrum Face Recognition

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Cross-spectrum face recognition, e.g. visible to thermal matching, remains a challenging task due to the large variation originated from different domains. This paper proposed a subspace projection hashing (SPH) to enable the cross-spectrum face recognition task. The intrinsic idea behind SPH is to project the features from different domains onto a common subspace, where matching the faces from different domains can be accomplished. Notably, we proposed a new loss function that can (i) preserve both inter-domain and intra-domain similarity; (ii) regularize a scaled-up pairwise distance between hashed codes, to optimize projection matrix. Three datasets, Wiki, EURECOM VIS-TH paired face and TDFace are adopted to evaluate the proposed SPH. The experimental results indicate that the proposed SPH outperforms the original linear subspace ranking hashing (LSRH) in the benchmark dataset (Wiki) and demonstrates a reasonably good performance for visible-thermal, visible-near-infrared face recognition, therefore suggests the feasibility and effectiveness of the proposed SPH.

Leveraging Quadratic Spherical Mutual Information Hashing for Fast Image Retrieval

Nikolaos Passalis, Anastasios Tefas

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Auto-TLDR; Quadratic Mutual Information for Large-Scale Hashing and Information Retrieval

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Several deep supervised hashing techniques have been proposed to allow for querying large image databases. However, it is often overlooked that the process of information retrieval can be modeled using information-theoretic metrics, leading to optimizing various proxies for the problem at hand instead. Contrary to this, we propose a deep supervised hashing algorithm that optimizes the learned codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of large-scale hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI), that is inspired by QMI, but leads to significant better retrieval precision. Indeed, the effectiveness of the proposed method is demonstrated under several different scenarios, using different datasets and network architectures, outperforming existing deep supervised image hashing techniques.

Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-To-Video Search

Savas Ozkan, Gözde Bozdağı Akar

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Auto-TLDR; Fast and Robust Image-to-Video Retrieval Using Local and Global Descriptors

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Cost-effective visual representation and fast query-by-example search are two challenging goals hat should be provided for web-scale visual retrieval task on a moderate hardware. In this paper, we introduce a fast yet robust method that ensures both of these goals by obtaining the state-of-the-art results for an image-to-video search scenario. To this end, we present important enhancements to commonly used indexing and visual representation techniques by promoting faster, better and more moderate retrieval performance. We also boost the effectiveness of the method for visual distortion by exploiting the individual decision results of local and global descriptors in the query time. By this way, local content descriptors effectively represent copied / duplicated scenes with large geometric deformations, while global descriptors for near duplicate and semantic searches are more practical. Experiments are conducted on the large-scale Stanford I2V dataset. The experimental results show that the method is effective in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. In addition, the proposed method is fairly accurate and achieves state-of-the-art performance based on the mAP score of the dataset. Lastly, we report additional mAP scores after updating the ground annotations obtained by the retrieval results of the proposed method showing more clearly the actual performance.

Double Manifolds Regularized Non-Negative Matrix Factorization for Data Representation

Jipeng Guo, Shuai Yin, Yanfeng Sun, Yongli Hu

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Auto-TLDR; Double Manifolds Regularized Non-negative Matrix Factorization for Clustering

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Non-negative matrix factorization (NMF) is an important method in learning latent data representation. The local geometrical structure can make the learned representation more effectively and significantly improve the performance of NMF. However, most of existing graph-based learning methods are determined by a predefined similarity graph which may be not optimal for specific tasks. To solve the above the problem, we propose the Double Manifolds Regularized NMF (DMR-NMF) model which jointly learns an adaptive affinity matrix with the non-negative matrix factorization. The learned affinity matrix can guide the NMF to fit the clustering task. Moreover, we develop the iterative updating optimization schemes for DMR-NMF, and provide the strict convergence proof of our optimization strategy. Empirical experiments on four different real-world data sets demonstrate the state-of-the-art performance of DMR-NMF in comparison with the other related algorithms.

Feature Extraction by Joint Robust Discriminant Analysis and Inter-Class Sparsity

Fadi Dornaika, Ahmad Khoder

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Auto-TLDR; Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA_FSIS)

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Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used for different classification tasks, these methods have some shortcomings. The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity. Furthermore, the targeted projection transformation focuses on the most discriminant original features. The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA_FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the $\ell_{2,1}$ constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.

Webly Supervised Image-Text Embedding with Noisy Tag Refinement

Niluthpol Mithun, Ravdeep Pasricha, Evangelos Papalexakis, Amit Roy-Chowdhury

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Auto-TLDR; Robust Joint Embedding for Image-Text Retrieval Using Web Images

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In this paper, we address the problem of utilizing web images in training robust joint embedding models for the image-text retrieval task. Prior webly supervised approaches directly leverage weakly annotated web images in the joint embedding learning framework. The objective of these approaches would suffer significantly when the ratio of noisy and missing tags associated with the web images is very high. In this regard, we propose a CP decomposition based tensor completion framework to refine the tags of web images by modeling observed ternary inter-relations between the sets of labeled images, tags, and web images as a tensor. To effectively deal with the high ratio of missing entries likely in our case, we incorporate intra-modal correlation as side information in the proposed framework. Our tag refinement approach combined with existing web supervised image-text embedding approaches provide a more principled way for learning the joint embedding models in the presence of significant noise from web data and limited clean labeled data. Experiments on benchmark datasets demonstrate that the proposed approach helps to achieve a significant performance gain in image-text retrieval.

Subspace Clustering Via Joint Unsupervised Feature Selection

Wenhua Dong, Xiaojun Wu, Hui Li, Zhenhua Feng, Josef Kittler

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Auto-TLDR; Unsupervised Feature Selection for Subspace Clustering

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Any high-dimensional data arising from practical applications usually contains irrelevant features, which may impact on the performance of existing subspace clustering methods. This paper proposes a novel subspace clustering method, which reconstructs the feature matrix by the means of unsupervised feature selection (UFS) to achieve a better dictionary for subspace clustering (SC). Different from most existing clustering methods, the proposed approach uses a reconstructed feature matrix as the dictionary rather than the original data matrix. As the feature matrix reconstructed by representative features is more discriminative and closer to the ground-truth, it results in improved performance. The corresponding non-convex optimization problem is effectively solved using the half-quadratic and augmented Lagrange multiplier methods. Extensive experiments on four real datasets demonstrate the effectiveness of the proposed method.

Embedding Shared Low-Rank and Feature Correlation for Multi-View Data Analysis

Zhan Wang, Lizhi Wang, Hua Huang

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Auto-TLDR; embedding shared low-rank and feature correlation for multi-view data analysis

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The diversity of multimedia data in the real-world usually forms multi-view features. How to explore the structure information and correlations among multi-view features is still an open problem. In this paper, we propose a novel multi-view subspace learning method, named embedding shared low-rank and feature correlation (ESLRFC), for multi-view data analysis. First, in the embedding subspace, we propose a robust low-rank model on each feature set and enforce a shared low-rank constraint to characterize the common structure information of multiple feature data. Second, we develop an enhanced correlation analysis in the embedding subspace for simultaneously removing the redundancy of each feature set and exploring the correlations of multiple feature data. Finally, we incorporate the low-rank model and the correlation analysis into a unified framework. The shared low-rank constraint not only depicts the data distribution consistency among multiple feature data, but also assists robust subspace learning. Experimental results on recognition tasks demonstrate the superior performance and noise robustness of the proposed method.

Low Rank Representation on Product Grassmann Manifolds for Multi-viewSubspace Clustering

Jipeng Guo, Yanfeng Sun, Junbin Gao, Yongli Hu, Baocai Yin

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Auto-TLDR; Low Rank Representation on Product Grassmann Manifold for Multi-View Data Clustering

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Clustering high dimension multi-view data with complex intrinsic properties and nonlinear manifold structure is a challenging task since these data are always embedded in low dimension manifolds. Inspired by Low Rank Representation (LRR), some researchers extended classic LRR on Grassmann manifold or Product Grassmann manifold to represent data with non-linear metrics. However, most of these methods utilized convex nuclear norm to leverage a low-rank structure, which was over-relaxation of true rank and would lead to the results deviated from the true underlying ones. And, the computational complexity of singular value decomposition of matrix is high for nuclear norm minimization. In this paper, we propose a new low rank model for high-dimension multi-view data clustering on Product Grassmann Manifold with the matrix tri-factorization which is used to control the upper bound of true rank of representation matrix. And, the original problem can be transformed into the nuclear norm minimization with smaller scale matrices. An effective solution and theoretical analysis are also provided. The experimental results show that the proposed method obviously outperforms other state-of-the-art methods on several multi-source human/crowd action video datasets.

RGB-Infrared Person Re-Identification Via Image Modality Conversion

Huangpeng Dai, Qing Xie, Yanchun Ma, Yongjian Liu, Shengwu Xiong

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Auto-TLDR; CE2L: A Novel Network for Cross-Modality Re-identification with Feature Alignment

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As a cross modality retrieval task, RGB-infrared person re-identification(Re-ID) is an important and challenging tasking, because of its important role in video surveillance applications and large cross-modality variations between visible and infrared images. Most previous works addressed the problem of cross-modality gap with feature alignment by original feature representation learning straightly. In this paper, different from existing works, we propose a novel network(CE2L) to tackle the cross-modality gap with feature alignment. CE2L mainly focuses on adding discriminative information and learning robust features by converting modality between visible and infrared images. Its merits are highlighted in two aspects: 1)Using CycleGAN to convert infrared images into color images can not only increase the recognition characteristics of images, but also allow the our network to better learn the two modal image features; 2)Our novel method can serve as data augmentation. Specifically, it can increase data diversity and total data against over-fitting by converting labeled training images to another modal images. Extensive experimental results on two datasets demonstrate superior performance compared to the baseline and the state-of-the-art methods.

Soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification

Fadi Dornaika, Abdullah Baradaaji, Youssof El Traboulsi

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Auto-TLDR; Semi-supervised Semi-Supervised Learning for Linear Feature Extraction and Label Propagation

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In recent times, graph-based semi-supervised learning proved to be a powerful paradigm for processing and mining large datasets. The main advantage relies on the fact that these methods can be useful in propagating a small set of known labels to a large set of unlabeled data. The scarcity of labeled data may affect the performance of the semi-learning. This paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation. The proposed method simultaneously estimates a discriminant transformation and the unknown label by exploiting both labeled and unlabeled data. In addition, the unknowns of the learning model are estimated by integrating two types of graph-based smoothness constraints. The resulting semi-supervised model is expected to learn more discriminative information. Experiments are conducted on six public image datasets. These experimental results show that the performance of the proposed method can be better than that of many state-of-the-art graph-based semi-supervised algorithms.

Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

Can Zhang, Hong Liu, Wei Guo, Mang Ye

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Auto-TLDR; Multi-Scale Part-Aware Cascading for RGB-Infrared Person Re-identification

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RGB-Infrared person re-identification (RGB-IR Re-ID) aims to matching persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in surveillance system under poor light conditions. Facing great challenges in complex variances including conventional single-modality and additional inter-modality discrepancies, most of existing RGB-IR Re-ID methods directly work on global features for simultaneous elimination, whereas modality-specific noises and modality-shared features are not well considered. To address these issues, a novel Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global in a cascading manner, which results in an unified representation robust to noises. Moreover, a marginal exponential center (MeCen) loss is introduced to jointly eliminate mixed variances, which enables to model cross-modality correlations on sharable salient features. Extensive experiments are conducted for demonstration that the proposed method outperforms all the state-of-the-arts by a large margin.

A CNN-RNN Framework for Image Annotation from Visual Cues and Social Network Metadata

Tobia Tesan, Pasquale Coscia, Lamberto Ballan

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Auto-TLDR; Context-Based Image Annotation with Multiple Semantic Embeddings and Recurrent Neural Networks

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Images represent a commonly used form of visual communication among people. Nevertheless, image classification may be a challenging task when dealing with unclear or non-common images needing more context to be correctly annotated. Metadata accompanying images on social-media represent an ideal source of additional information for retrieving proper neighborhoods easing image annotation task. To this end, we blend visual features extracted from neighbors and their metadata to jointly leverage context and visual cues. Our models use multiple semantic embeddings to achieve the dual objective of being robust to vocabulary changes between train and test sets and decoupling the architecture from the low-level metadata representation. Convolutional and recurrent neural networks (CNNs-RNNs) are jointly adopted to infer similarity among neighbors and query images. We perform comprehensive experiments on the NUS-WIDE dataset showing that our models outperform state-of-the-art architectures based on images and metadata, and decrease both sensory and semantic gaps to better annotate images.

JECL: Joint Embedding and Cluster Learning for Image-Text Pairs

Sean Yang, Kuan-Hao Huang, Bill Howe

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Auto-TLDR; JECL: Clustering Image-Caption Pairs with Parallel Encoders and Regularized Clusters

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We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster assignments. These image-caption pairs arise frequently in high-value applications where structured training data is expensive to produce, but free-text descriptions are common. JECL trains by minimizing the Kullback-Leibler divergence between the distribution of the images and text to that of a combined joint target distribution and optimizing the Jensen-Shannon divergence between the soft cluster assignments of the images and text. Regularizers are also applied to JECL to prevent trivial solutions. Experiments show that JECL outperforms both single-view and multi-view methods on large benchmark image-caption datasets, and is remarkably robust to missing captions and varying data sizes.

Learning Sign-Constrained Support Vector Machines

Kenya Tajima, Kouhei Tsuchida, Esmeraldo Ronnie Rey Zara, Naoya Ohta, Tsuyoshi Kato

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Auto-TLDR; Constrained Sign Constraints for Learning Linear Support Vector Machine

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Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes O(nd) computational cost and the sublinear convergence of the objective error is guaranteed. The second algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd) cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an epsilon-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.

Hybrid Decomposition Convolution Neural Network and Vocabulary Forest for Image Retrieval

Djenouri Youcef, Jon Hjelmervik

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Auto-TLDR; DCNN-vForest: Convolutional Neural Network and Vocabulary Forest for Efficient Image Retrieval

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This paper introduces a highly efficient image retrieval technique called DCNN-vForest (Decomposition Convolution Neural Network and vocabulary Forest), which aims to retrieve the relevant images to the given image query by studying the correlation between images in the image database based on decomposition. The regional and global features of the image database are first extracted using the convolution neural network, and then divided into similar clusters using the Kmeans algorithm. We propose a new structure called vForest (vocabulary Forest), by calculating the vocabulary tree on each cluster of images. The retrieval process benefits from the knowledge provided by the vForest, and instead of considering the whole image database, only the most similar clusters to the image query are explored. To demonstrate the usefulness of our approach, intensive experiments have been carried out on ground-truth image databases, the results reveal the superiority of DCNN-vForest against the baseline image retrieval solutions, in terms of runtime and accuracy.

Deep Composer: A Hash-Based Duplicative Neural Network for Generating Multi-Instrument Songs

Jacob Galajda, Brandon Royal, Kien Hua

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Auto-TLDR; Deep Composer for Intelligence Duplication

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Music is one of the most appreciated forms of art, and generating songs has become a popular subject in the artificial intelligence community. There are various networks that can produce pleasant sounding music, but no model has been able to produce music that duplicates the style of a specific artist or artists. In this paper, we extend a previous single-instrument model: the Deep Composer -a model we believe to be capable of achieving this. Deep Composer originates from the Deep Segment Hash Learning (DSHL) single instrument model and is designed to learn how a specific artist would place individual segments of music together rather than create music similar to a specific genre. To the best of our knowledge, no other network has been designed to achieve this. For these reasons, we introduce a new field of study, Intelligence Duplication (ID). AI research generally focuses on developing techniques to mimic universal intelligence. Intelligence Duplication (ID) research focuses on techniques to artificially duplicate or clone a specific mind such as Mozart. Additionally, we present a new retrieval algorithm, Segment Barrier Retrieval (SBR), to improve retrieval accuracy within the hash-space as opposed to a more traditionally used feature-space. SBR prevents retrieval branches from entering areas of low-density within the hash-space, a phenomena we identify and label as segment sparsity. To test our Deep Composer and the effectiveness of SBR, we evaluate various models with different SBR threshold values and conduct qualitative surveys for each model. The survey results indicate that our Deep Composer model is capable of learning music generation from multiple composers. Our extended Deep Composer model provides a more suitable platform for Intelligence Duplication. Future work can apply this platform to duplicate great composers such as Mozart or allow them to collaborate in the virtual space.

Heterogeneous Graph-Based Knowledge Transfer for Generalized Zero-Shot Learning

Junjie Wang, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenjie Zhang, Hongyuan Zha

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Auto-TLDR; Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-Shot Learning

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Generalized zero-shot learning (GZSL) tackles the problem of learning to classify instances involving both seen classes and unseen ones. The key issue is how to effectively transfer the model learned from seen classes to unseen classes. Existing works in GZSL usually assume that some prior information about unseen classes are available. However, such an assumption is unrealistic when new unseen classes appear dynamically. To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network. Specifically, a structured heterogeneous graph is constructed with high-level representative nodes for seen classes, which are chosen through Wasserstein barycenter in order to simultaneously capture inter-class and intra-class relationship. The aggregation and embedding functions can be learned throughgraph neural network, which can be used to compute the embeddings of unseen classes by transferring the knowledge from their neighbors. Extensive experiments on public benchmark datasets show that our method achieves state-of-the-art results.

Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization

Li Ren, Kai Li, Liqiang Wang, Kien Hua

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Auto-TLDR; Adversarial Discriminative Domain Regularization for Efficient Cross-Modal Matching

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Matching information across image and text modalities is a fundamental challenge for many applications that involve both vision and natural language processing. The objective is to find efficient similarity metrics to compare the similarity between visual and textual information. Existing approaches mainly match the local visual objects and the sentence words in a shared space with attention mechanisms. The matching performance is still limited because the similarity computation is based on simple comparisons of the matching features, ignoring the characteristics of their distribution in the data. In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words. Specifically, we propose a novel Adversarial Discriminative Domain Regularization (ADDR) learning framework, beyond the paradigm metric learning objective, to construct a set of discriminative data domains within each image-text pairs. Our approach can generally improve the learning efficiency and the performance of existing metrics learning frameworks by regulating the distribution of the hidden space between the matching pairs. The experimental results show that this new approach significantly improves the overall performance of several popular cross-modal matching techniques (SCAN, VSRN, BFAN) on the MS-COCO and Flickr30K benchmarks.

Feature Extraction and Selection Via Robust Discriminant Analysis and Class Sparsity

Ahmad Khoder, Fadi Dornaika

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Auto-TLDR; Hybrid Linear Discriminant Embedding for supervised multi-class classification

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The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multi-class classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Inter-class sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix. The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.

A Spectral Clustering on Grassmann Manifold Via Double Low Rank Constraint

Xinglin Piao, Yongli Hu, Junbin Gao, Yanfeng Sun, Xin Yang, Baocai Yin

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Auto-TLDR; Double Low Rank Representation for High-Dimensional Data Clustering on Grassmann Manifold

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High-dimension data clustering is a fundamental topic in machine learning and data mining areas. In recent year, researchers have proposed a series of effective methods based on Low Rank Representation (LRR) which could explore low-dimension subspace structure embedded in original data effectively. The traditional LRR methods usually treat original data as samples in Euclidean space. They generally adopt linear metric to measure the distance between two data. However, high-dimension data (such as video clip or imageset) are always considered as non-linear manifold data such as Grassmann manifold. Therefore, the traditional linear Euclidean metric would be no longer suitable for these special data. In addition, traditional LRR clustering method always adopt nuclear norm as low rank constraint which would lead to suboptimal solution and decrease the clustering accuracy. In this paper, we proposed a new low rank method on Grassmann manifold for high-dimension data clustering task. In the proposed method, a double low rank representation approach is proposed by combining the nuclear norm and bilinear representation for better construct the representation matrix. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods.

RWMF: A Real-World Multimodal Foodlog Database

Pengfei Zhou, Cong Bai, Kaining Ying, Jie Xia, Lixin Huang

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Auto-TLDR; Real-World Multimodal Foodlog: A Real-World Foodlog Database for Diet Assistant

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With the increasing health concerns on diet, it's worthwhile to develop an intelligent assistant that can help users eat healthier. Such assistants can automatically give personal advice for the users' diet and generate health reports about eating on a regular basis. To boost the research on such diet assistant, we establish a real-world foodlog database using various methods such as filter, cluster and graph convolutional network. This database is built based on real-world lifelog and medical data, which is named as Real-World Multimodal Foodlog (RWMF). It contains 7500 multimodal pairs, and each pair consists of a food image paired with a line of personal biometrics data (such as Blood Glucose) and a textual food description of food composition paired with a line of food nutrition data. In this paper, we present the detailed procedures for setting up the database. We evaluate the performance of RWMF using different food classification and cross-modal retrieval approaches. We also test the performance of multimodal fusion on RWMF through ablation experiments. The experimental results show that the RWMF database is quite challenging and can be widely used to evaluate the performance of food analysis methods based on multimodal data.

A Unified Framework for Distance-Aware Domain Adaptation

Fei Wang, Youdong Ding, Huan Liang, Yuzhen Gao, Wenqi Che

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Auto-TLDR; distance-aware domain adaptation

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Unsupervised domain adaptation has achieved significant results by leveraging knowledge from a source domain to learn a related but unlabeled target domain. Previous methods are insufficient to model domain discrepancy and class discrepancy, which may lead to misalignment and poor adaptation performance. To address this problem, in this paper, we propose a unified framework, called distance-aware domain adaptation, which is fully aware of both cross-domain distance and class-discriminative distance. In addition, second-order statistics distance and manifold alignment are also exploited to extract more information from data. In this manner, the generalization error of the target domain in classification problems can be reduced substantially. To validate the proposed method, we conducted experiments on five public datasets and an ablation study. The results demonstrate the good performance of our proposed method.

Nonlinear Ranking Loss on Riemannian Potato Embedding

Byung Hyung Kim, Yoonje Suh, Honggu Lee, Sungho Jo

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Auto-TLDR; Riemannian Potato for Rank-based Metric Learning

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We propose a rank-based metric learning method by leveraging a concept of the Riemannian Potato for better separating non-linear data. By exploring the geometric properties of Riemannian manifolds, the proposed loss function optimizes the measure of dispersion using the distribution of Riemannian distances between a reference sample and neighbors and builds a ranked list according to the similarities. We show the proposed function can learn a hypersphere for each class, preserving the similarity structure inside it on Riemannian manifold. As a result, compared with Euclidean distance-based metric, our method can further jointly reduce the intra-class distances and enlarge the inter-class distances for learned features, consistently outperforming state-of-the-art methods on three widely used non-linear datasets.

Supervised Feature Embedding for Classification by Learning Rank-Based Neighborhoods

Ghazaal Sheikhi, Hakan Altincay

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Auto-TLDR; Supervised Feature Embedding with Representation Learning of Rank-based Neighborhoods

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In feature embedding, the recovery of associated discriminative information in the reduced subspace is critical for downstream classifiers. In this study, a supervised feature embedding method is proposed inspired by the well-known word embedding technique, word2vec. Proposed embedding method is implemented as representative learning of rank-based neighborhoods. The notion of context words in word2vec is extended into neighboring instances within a given window. Neighborship is defined using ranks of instances rather than their values so that regions with different densities are captured properly. Each sample is represented by a unique one-hot vector whereas its neighbors are encoded by several two-hot vectors. The two-hot vectors are identical for neighboring samples of the same class. A feed-forward neural network with a continuous projection layer, then learns the mapping from one-hot vectors to multiple two-hot vectors. The hidden layer determines the reduced subspace for the train samples. The obtained transformation is then applied on test data to find a lower-dimensional representation. Proposed method is tested in classification problems on 10 UCI data sets. Experimental results confirm that the proposed method is effective in finding a discriminative representation of the features and outperforms several supervised embedding approaches in terms of classification performance.

Deep Convolutional Embedding for Digitized Painting Clustering

Giovanna Castellano, Gennaro Vessio

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Auto-TLDR; A Deep Convolutional Embedding Model for Clustering Artworks

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Clustering artworks is difficult because of several reasons. On one hand, recognizing meaningful patterns in accordance with domain knowledge and visual perception is extremely hard. On the other hand, the application of traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, we propose to use a deep convolutional embedding model for digitized painting clustering, in which the task of mapping the input raw data to an abstract, latent space is jointly optimized with the task of finding a set of cluster centroids in this latent feature space. Quantitative and qualitative experimental results show the effectiveness of the proposed method. The model is also able to outperform other state-of-the-art deep clustering approaches to the same problem. The proposed method may be beneficial to several art-related tasks, particularly visual link retrieval and historical knowledge discovery in painting datasets.

A Base-Derivative Framework for Cross-Modality RGB-Infrared Person Re-Identification

Hong Liu, Ziling Miao, Bing Yang, Runwei Ding

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Auto-TLDR; Cross-modality RGB-Infrared Person Re-identification with Auxiliary Modalities

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Cross-modality RGB-infrared (RGB-IR) person re-identification (Re-ID) is a challenging research topic due to the heterogeneity of RGB and infrared images. In this paper, we aim to find some auxiliary modalities, which are homologous with the visible or infrared modalities, to help reduce the modality discrepancy caused by heterogeneous images. Accordingly, a new base-derivative framework is proposed, where base refers to the original visible and infrared modalities, and derivative refers to the two auxiliary modalities that are derived from base. In the proposed framework, the double-modality cross-modal learning problem is reformulated as a four-modality one. After that, the images of all the base and derivative modalities are fed into the feature learning network. With the doubled input images, the learned person features become more discriminative. Furthermore, the proposed framework is optimized by the enhanced intra- and cross-modality constraints with the assistance of two derivative modalities. Experimental results on two publicly available datasets SYSU-MM01 and RegDB show that the proposed method outperforms the other state-of-the-art methods. For instance, we achieve a gain of over 13\% in terms of both Rank-1 and mAP on RegDB dataset.

More Correlations Better Performance: Fully Associative Networks for Multi-Label Image Classification

Yaning Li, Liu Yang

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Auto-TLDR; Fully Associative Network for Fully Exploiting Correlation Information in Multi-Label Classification

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Recent researches demonstrate that correlation modeling plays a key role in high-performance multi-label classification methods. However, existing methods do not take full advantage of correlation information, especially correlations in feature and label spaces of each image, which limits the performance of correlation-based multi-label classification methods. With more correlations considered, in this study, a Fully Associative Network (FAN) is proposed for fully exploiting correlation information, which involves both visual feature and label correlations. Specifically, FAN introduces a robust covariance pooling to summarize convolution features as global image representation for capturing feature correlation in the multi-label task. Moreover, it constructs an effective label correlation matrix based on a re-weighted scheme, which is fed into a graph convolution network for capturing label correlation. Then, correlation between covariance representations (i.e., feature correlation ) and the outputs of GCN (i.e., label correlation) are modeled for final prediction. Experimental results on two datasets illustrate the effectiveness and efficiency of our proposed FAN compared with state-of-the-art methods.

Attention-Based Deep Metric Learning for Near-Duplicate Video Retrieval

Kuan-Hsun Wang, Chia Chun Cheng, Yi-Ling Chen, Yale Song, Shang-Hong Lai

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Auto-TLDR; Attention-based Deep Metric Learning for Near-duplicate Video Retrieval

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Near-duplicate video retrieval (NDVR) is an important and challenging problem due to the increasing amount of videos uploaded to the Internet. In this paper, we propose an attention-based deep metric learning method for NDVR. Our method is based on well-established principles: We leverage two-stream networks to combine RGB and optical flow features, and incorporate an attention module to effectively deal with distractor frames commonly observed in near duplicate videos. We further aggregate the features corresponding to multiple video segments to enhance the discriminative power. The whole system is trained using a deep metric learning objective with a Siamese architecture. Our experiments show that the attention module helps eliminate redundant and noisy frames, while focusing on visually relevant frames for solving NVDR. We evaluate our approach on recent large-scale NDVR datasets, CC_WEB_VIDEO, VCDB, FIVR and SVD. To demonstrate the generalization ability of our approach, we report results in both within- and cross-dataset settings, and show that the proposed method significantly outperforms state-of-the-art approaches.

Fast Subspace Clustering Based on the Kronecker Product

Lei Zhou, Xiao Bai, Liang Zhang, Jun Zhou, Edwin Hancock

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Auto-TLDR; Subspace Clustering with Kronecker Product for Large Scale Datasets

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Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N^3) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN^{3/k}). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

SoftmaxOut Transformation-Permutation Network for Facial Template Protection

Hakyoung Lee, Cheng Yaw Low, Andrew Teoh

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Auto-TLDR; SoftmaxOut Transformation-Permutation Network for C cancellable Biometrics

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In this paper, we propose a data-driven cancellable biometrics scheme, referred to as SoftmaxOut Transformation-Permutation Network (SOTPN). The SOTPN is a neural version of Random Permutation Maxout (RPM) transform, which was introduced for facial template protection. We present a specialized SoftmaxOut layer integrated with the permutable MaxOut units and the parameterized softmax function to approximate the non-differentiable permutation and the winner-takes-all operations in the RPM transform. On top of that, a novel pairwise ArcFace loss and a code balancing loss are also formulated to ensure that the SOTPN-transformed facial template is cancellable, discriminative, high entropy and free from quantization errors when coupled with the SoftmaxOut layer. The proposed SOTPN is evaluated on three face datasets, namely LFW, YouTube Face and Facescrub, and our experimental results disclosed that the SOTPN outperforms the RPM transform significantly.

Exploiting Knowledge Embedded Soft Labels for Image Recognition

Lixian Yuan, Riquan Chen, Hefeng Wu, Tianshui Chen, Wentao Wang, Pei Chen

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Auto-TLDR; A Soft Label Vector for Image Recognition

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Objects from correlated classes usually share highly similar appearances while objects from uncorrelated classes are very different. Most of current image recognition works treat each class independently, which ignores these class correlations and inevitably leads to sub-optimal performance in many cases. Fortunately, object classes inherently form a hierarchy with different levels of abstraction and this hierarchy encodes rich correlations among different classes. In this work, we utilize a soft label vector that encodes the prior knowledge of class correlations as extra regularization to train the image classifiers. Specifically, for each class, instead of simply using a one-hot vector, we assign a high value to its correlated classes and assign small values to those uncorrelated ones, thus generating knowledge embedded soft labels. We conduct experiments on both general and fine-grained image recognition benchmarks and demonstrate its superiority compared with existing methods.

Constrained Spectral Clustering Network with Self-Training

Xinyue Liu, Shichong Yang, Linlin Zong

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Auto-TLDR; Constrained Spectral Clustering Network: A Constrained Deep spectral clustering network

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Deep spectral clustering networks have shown their superiorities due to the integration of feature learning and cluster assignment, and the ability to deal with non-convex clusters. Nevertheless, deep spectral clustering is still an ill-posed problem. Specifically, the affinity learned by the most remarkable SpectralNet is not guaranteed to be consistent with local invariance and thus hurts the final clustering performance. In this paper, we propose a novel framework of Constrained Spectral Clustering Network (CSCN) by incorporating pairwise constraints and clustering oriented fine-tuning to deal with the ill-posedness. To the best of our knowledge, this is the first constrained deep spectral clustering method. Another advantage of CSCN over existing constrained deep clustering networks is that it propagates pairwise constraints throughout the entire dataset. In addition, we design a clustering oriented loss by self-training to simultaneously finetune feature representations and perform cluster assignments, which further improve the quality of clustering. Extensive experiments on benchmark datasets demonstrate that our approach outperforms the state-of-the-art clustering methods.

Classification and Feature Selection Using a Primal-Dual Method and Projections on Structured Constraints

Michel Barlaud, Antonin Chambolle, Jean_Baptiste Caillau

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Auto-TLDR; A Constrained Primal-dual Method for Structured Feature Selection on High Dimensional Data

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This paper deals with feature selection using supervised classification on high dimensional datasets. A classical approach is to project data on a low dimensional space and classify by minimizing an appropriate quadratic cost. Our first contribution is to introduce a matrix of centers in the definition of this cost. Moreover, as quadratic costs are not robust to outliers, we propose to use an $\ell_1$ cost instead (or Huber loss to mitigate overfitting issues). While control on sparsity is commonly obtained by adding an $\ell_1$ constraint on the vectorized matrix of weights used for projecting the data, our second contribution is to enforce structured sparsity. To this end we propose constraints that take into account the matrix structure of the data, based either on the nuclear norm, on the $\ell_{2,1}$ norm, or on the $\ell_{1,2}$ norm for which we provide a new projection algorithm. We optimize simultaneously the projection matrix and the matrix of centers thanks to a new tailored constrained primal-dual method. The primal-dual framework is general enough to encompass the various robust losses and structured constraints we use, and allows a convergence analysis. We demonstrate the effectiveness of the approach on three biological datasets. Our primal-dual method with robust losses, adaptive centers and structured constraints does significantly better than classical methods, both in terms of accuracy and computational time.

Complementing Representation Deficiency in Few-Shot Image Classification: A Meta-Learning Approach

Xian Zhong, Cheng Gu, Wenxin Huang, Lin Li, Shuqin Chen, Chia-Wen Lin

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Auto-TLDR; Meta-learning with Complementary Representations Network for Few-Shot Learning

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Few-shot learning is a challenging problem that has attracted more and more attention recently since abundant training samples are difficult to obtain in practical applications. Meta-learning has been proposed to address this issue, which focuses on quickly adapting a predictor as a base-learner to new tasks, given limited labeled samples. However, a critical challenge for meta-learning is the representation deficiency since it is hard to discover common information from a small number of training samples or even one, as is the representation of key features from such little information. As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks. Existing methods mostly resort to extracting less expressive features so as to avoid the representation deficiency. Aiming at learning better representations, we propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification. In particular, we embed a latent space, where latent codes are reconstructed with extra representation information to complement the representation deficiency. Furthermore, the latent space is established with variational inference, collaborating well with different base-learners, and can be extended to other models. Finally, our end-to-end framework achieves the state-of-the-art performance in image classification on three standard few-shot learning datasets.

A Flatter Loss for Bias Mitigation in Cross-Dataset Facial Age Estimation

Ali Akbari, Muhammad Awais, Zhenhua Feng, Ammarah Farooq, Josef Kittler

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Auto-TLDR; Cross-dataset Age Estimation for Neural Network Training

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Existing studies in facial age estimation have mostly focused on intra-dataset protocols that assume training and test images captured under similar conditions. However, this is rarely valid in practical applications, where training and test sets usually have different characteristics. In this paper, we advocate a cross-dataset protocol for age estimation benchmarking. In order to improve the cross-dataset age estimation performance, we mitigate the inherent bias caused by the learning algorithm. To this end, we propose a novel loss function that is more effective for neural network training. The relative smoothness of the proposed loss function is its advantage with regards to the optimisation process performed by stochastic gradient decent. Its lower gradient, compared with existing loss functions, facilitates the discovery of and convergence to a better optimum, and consequently a better generalisation. The cross-dataset experimental results demonstrate the superiority of the proposed method over the state-of-the-art algorithms in terms of accuracy and generalisation capability.

Cancelable Biometrics Vault: A Secure Key-Binding Biometric Cryptosystem Based on Chaffing and Winnowing

Osama Ouda, Karthik Nandakumar, Arun Ross

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Auto-TLDR; Cancelable Biometrics Vault for Key-binding Biometric Cryptosystem Framework

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Existing key-binding biometric cryptosystems, such as the Fuzzy Vault Scheme (FVS) and Fuzzy Commitment Scheme (FCS), employ Error Correcting Codes (ECC) to handle intra-user variations in biometric data. As a result, a trade-off exists between the key length and matching accuracy. Moreover, these systems are vulnerable to privacy leakage, i.e., it is trivial to recover the original biometric template given the secure sketch and its associated cryptographic key. In this work, we propose a novel key-binding biometric cryptosystem framework, referred to as Cancelable Biometrics Vault (CBV), to address the above two limitations. The CBV framework is inspired by the cryptographic principle of chaffing and winnowing. It utilizes the concept of cancelable biometrics (CB) to generate secure biometric templates, which in turn are used to encode bits in a cryptographic key. While the CBV framework is generic and does not rely on a specific biometric representation, it does assume the availability of a suitable (satisfying the requirements of accuracy preservation, non-invertibility, and non-linkability) CB scheme for the given representation. To demonstrate the usefulness of the proposed CBV framework, we implement this approach using an extended BioEncoding scheme, which is a CB scheme appropriate for bit strings such as iris-codes. Unlike the baseline BioEncoding scheme, the extended version proposed in this work fulfills all the three requirements of a CB construct. Experiments show that the decoding accuracy of the proposed CBV framework is comparable to the recognition accuracy of the underlying CB construct, namely, the extended BioEncoding scheme, regardless of the cryptographic key size.

A Prototype-Based Generalized Zero-Shot Learning Framework for Hand Gesture Recognition

Jinting Wu, Yujia Zhang, Xiao-Guang Zhao

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Auto-TLDR; Generalized Zero-Shot Learning for Hand Gesture Recognition

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Hand gesture recognition plays a significant role in human-computer interaction for understanding various human gestures and their intent. However, most prior works can only recognize gestures of limited labeled classes and fail to adapt to new categories. The task of Generalized Zero-Shot Learning (GZSL) for hand gesture recognition aims to address the above issue by leveraging semantic representations and detecting both seen and unseen class samples. In this paper, we propose an end-to-end prototype-based GZSL framework for hand gesture recognition which consists of two branches. The first branch is a prototype-based detector that learns gesture representations and determines whether an input sample belongs to a seen or unseen category. The second branch is a zero-shot label predictor which takes the features of unseen classes as input and outputs predictions through a learned mapping mechanism between the feature and the semantic space. We further establish a hand gesture dataset that specifically targets this GZSL task, and comprehensive experiments on this dataset demonstrate the effectiveness of our proposed approach on recognizingQuestionnaire both seen and unseen gestures.

Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Jiehui Deng, Sheng Wan, Xiang Wang, Enmei Tu, Xiaolin Huang, Jie Yang, Chen Gong

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Auto-TLDR; EAGAT: Edge-Aware Graph Attention Network for Automatic REU Estimation in Mobile Networks

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Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.