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Open Set Domain Recognition Via Attention-Based GCN and Semantic Matching Optimization

Xinxing He, Yuan Yuan, Zhiyu Jiang

Auto-TLDR; Attention-based GCN and Semantic Matching Optimization for Open Set Domain Recognition

Abstract Slides Poster

Open set domain recognition has got the attention in recent years. The task aims to specifically classify each sample in the practical unlabeled target domain, which consists of all known classes in the manually labeled source domain and target-specific unknown categories. The absence of annotated training data or auxiliary attribute information for unknown categories makes this task especially difficult. Moreover, exiting domain discrepancy in label space and data distribution further distracts the knowledge transferred from known classes to unknown classes. To address these issues, this work presents an end-to-end model based on attention-based GCN and semantic matching optimization, which first employs the attention mechanism to enable the central node to learn more discriminating representations from its neighbors in the knowledge graph. Moreover, a coarse-to-fine semantic matching optimization approach is proposed to progressively bridge the domain gap. Experimental results validate that the proposed model not only has superiority on recognizing the images of known and unknown classes, but also can adapt to various openness of the target domain.

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Zero-Shot Text Classification with Semantically Extended Graph Convolutional Network

Tengfei Liu, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin

Auto-TLDR; Semantically Extended Graph Convolutional Network for Zero-shot Text Classification

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As a challenging task of Natural Language Processing(NLP), zero-shot text classification has attracted more and more attention recently. It aims to detect classes that the model has never seen in the training set. For this purpose, a feasible way is to construct connection between the seen and unseen classes by semantic extension and classify the unseen classes by information propagation over the connection. Although many related zero-shot text classification methods have been exploited, how to realize semantic extension properly and propagate information effectively is far from solved. In this paper, we propose a novel zero-shot text classification method called Semantically Extended Graph Convolutional Network (SEGCN). In the proposed method, the semantic category knowledge from ConceptNet is utilized to semantic extension for linking seen classes to unseen classes and constructing a graph of all classes. Then, we build upon Graph Convolutional Network (GCN) for predicting the textual classifier for each category, which transfers the category knowledge by the convolution operators on the constructed graph and is trained in a semi-supervised manner using the samples of the seen classes. The experimental results on Dbpedia and 20newsgroup datasets show that our method outperforms the state of the art zero-shot text classification methods.

Unsupervised Multi-Task Domain Adaptation

Shih-Min Yang, Mei-Chen Yeh

Auto-TLDR; Unsupervised Domain Adaptation with Multi-task Learning for Image Recognition

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With abundant labeled data, deep convolutional neural networks have shown great success in various image recognition tasks. However, these models are often less powerful when applied to novel datasets due to a phenomenon known as domain shift. Unsupervised domain adaptation methods aim to address this problem, allowing deep models trained on the labeled source domain to be used on a different target domain (without labels). In this paper, we investigate whether the generalization ability of an unsupervised domain adaptation method can be improved through multi-task learning, with learned features required to be both domain invariant and discriminative for multiple different but relevant tasks. Experiments evaluating two fundamental recognition tasks---including image recognition and segmentation--- show that the generalization ability empowered by multi-task learning may not benefit recognition when the model is directly applied on the target domain, but the multi-task setting can boost the performance of state-of-the-art unsupervised domain adaptation methods by a non-negligible margin.

Teacher-Student Competition for Unsupervised Domain Adaptation

Ruixin Xiao, Zhilei Liu, Baoyuan Wu

Auto-TLDR; Unsupervised Domain Adaption with Teacher-Student Competition

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With the supervision from source domain only in class-level, existing unsupervised domain adaption (UDA) methods mainly learn the domain-invariant representations from a shared feature extractor, which cause the source-bias problem. This paper proposes an unsupervised domain adaption approach with Teacher-Student Competition (TSC). In particular, a student network is introduced to learn the target-specific feature space, and we design a novel competition mechanism to select more credible pseudo-labels for the training of student network. We introduce a teacher network with the structure of existing conventional UDA method, and both teacher and student networks compete to provide target pseudo-labels to constrain every target sample's training in student network. Extensive experiments demonstrate that our proposed TSC framework significantly outperforms the state-of-the-art domain adaption methods on Office-31 and ImageCLEF-DA benchmarks.

Class Conditional Alignment for Partial Domain Adaptation

Mohsen Kheirandishfard, Fariba Zohrizadeh, Farhad Kamangar

Auto-TLDR; Multi-class Adversarial Adaptation for Partial Domain Adaptation

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Adversarial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the source label space. The main purpose of PDA is to identify the shared classes between the domains and promote learning transferable knowledge from these classes. In this paper, we propose a multi-class adversarial architecture for PDA. The proposed approach jointly aligns the marginal and class-conditional distributions in the shared label space by minimaxing a novel multi-class adversarial loss function. Furthermore, we incorporate effective regularization terms to encourage selecting the most relevant subset of source domain classes. In the absence of target labels, the proposed approach is able to effectively learn domain-invariant feature representations, which in turn can enhance the classification performance in the target domain. Comprehensive experiments on three benchmark datasets Office-$31$, Office-Home, and Caltech-Office corroborate the effectiveness of the proposed approach in addressing different partial transfer learning tasks.

Adversarially Constrained Interpolation for Unsupervised Domain Adaptation

Mohamed Azzam, Aurele Tohokantche Gnanha, Hau-San Wong, Si Wu

Auto-TLDR; Unsupervised Domain Adaptation with Domain Mixup Strategy

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We address the problem of unsupervised domain adaptation (UDA) which aims at adapting models trained on a labeled domain to a completely unlabeled domain. One way to achieve this goal is to learn a domain-invariant representation. However, this approach is subject to two challenges: samples from two domains are insufficient to guarantee domain-invariance at most part of the latent space, and neighboring samples from the target domain may not belong to the same class on the low-dimensional manifold. To mitigate these shortcomings, we propose two strategies. First, we incorporate a domain mixup strategy in domain adversarial learning model by linearly interpolating between the source and target domain samples. This allows the latent space to be continuous and yields an improvement of the domain matching. Second, the domain discriminator is regularized via judging the relative difference between both domains for the input mixup features, which speeds up the domain matching. Experiment results show that our proposed model achieves a superior performance on different tasks under various domain shifts and data complexity.

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

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.

A Unified Framework for Distance-Aware Domain Adaptation

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

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.

Supervised Domain Adaptation Using Graph Embedding

Lukas Hedegaard, Omar Ali Sheikh-Omar, Alexandros Iosifidis

Auto-TLDR; Domain Adaptation from the Perspective of Multi-view Graph Embedding and Dimensionality Reduction

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Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of multi-view graph embedding and dimensionality reduction. Instead of solving the generalised eigenvalue problem to perform the embedding, we formulate the graph-preserving criterion as loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework which generalises the prior methods CCSA and d-SNE, and enables simple and effective loss designs; an LDA-inspired instantiation of the framework leads to performance on par with the state-of-the-art on the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.

Prior Knowledge about Attributes: Learning a More Effective Potential Space for Zero-Shot Recognition

Chunlai Chai, Yukuan Lou

Auto-TLDR; Attribute Correlation Potential Space Generation for Zero-Shot Learning

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Zero-shot learning (ZSL) aims to recognize unseen classes accurately by learning seen classes and known attributes, but correlations in attributes were ignored by previous study which lead to classification results confused. To solve this problem, we build an Attribute Correlation Potential Space Generation (ACPSG) model which uses a graph convolution network and attribute correlation to generate a more discriminating potential space. Combining potential discrimination space and user-defined attribute space, we can better classify unseen classes. Our approach outperforms some existing state-of-the-art methods on several benchmark datasets, whether it is conventional ZSL or generalized ZSL.

Cross-Domain Semantic Segmentation of Urban Scenes Via Multi-Level Feature Alignment

Bin Zhang, Shengjie Zhao, Rongqing Zhang

Auto-TLDR; Cross-Domain Semantic Segmentation Using Generative Adversarial Networks

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Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.

Randomized Transferable Machine

Pengfei Wei, Tze Yun Leong

Auto-TLDR; Randomized Transferable Machine for Suboptimal Feature-based Transfer Learning

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Feature-based transfer method is one of the most effective methodologies for transfer learning. Existing works usually claim the learned new feature representation is truly \emph{domain-invariant}, and thus directly train a transfer model $\mathcal{M}$ on source domain. In this paper, we work on a more realistic scenario where the new feature representation is suboptimal where small divergence still exists across domains. We propose a new learning strategy and name the transfer model following the learning strategy as Randomized Transferable Machine (RTM). More specifically, we work on source data with the new feature representation learned from existing feature-based transfer methods. Our key idea is to enlarge source training data populations by randomly corrupting source data using some noises, and then train a transfer model $\widetilde{\mathcal{M}}$ performing well on all these corrupted source data populations. In principle, the more corruptions are made, the higher probability of the target data can be covered by the constructed source populations and thus a better transfer performance can be achieved by $\widetilde{\mathcal{M}}$. An ideal case is with infinite corruptions, which however is infeasible in reality. We instead develop a marginalized solution. With a marginalization trick, we can train an RTM that is equivalently trained using infinite source noisy populations without truly conducting any corruption. More importantly, such an RTM has a closed-form solution, which enables a super fast and efficient training. Extensive experiments on various real-world transfer tasks show that RTM is a very promising transfer model.

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

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

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.

Semi-Supervised Domain Adaptation Via Selective Pseudo Labeling and Progressive Self-Training

Yoonhyung Kim, Changick Kim

Auto-TLDR; Semi-supervised Domain Adaptation with Pseudo Labels

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Domain adaptation (DA) is a representation learning methodology that transfers knowledge from a label-sufficient source domain to a label-scarce target domain. While most of early methods are focused on unsupervised DA (UDA), several studies on semi-supervised DA (SSDA) are recently suggested. In SSDA, a small number of labeled target images are given for training, and the effectiveness of those data is demonstrated by the previous studies. However, the previous SSDA approaches solely adopt those data for embedding ordinary supervised losses, overlooking the potential usefulness of the few yet informative clues. Based on this observation, in this paper, we propose a novel method that further exploits the labeled target images for SSDA. Specifically, we utilize labeled target images to selectively generate pseudo labels for unlabeled target images. In addition, based on the observation that pseudo labels are inevitably noisy, we apply a label noise-robust learning scheme, which progressively updates the network and the set of pseudo labels by turns. Extensive experimental results show that our proposed method outperforms other previous state-of-the-art SSDA methods.

Text Recognition in Real Scenarios with a Few Labeled Samples

Jinghuang Lin, Cheng Zhanzhan, Fan Bai, Yi Niu, Shiliang Pu, Shuigeng Zhou

Auto-TLDR; Few-shot Adversarial Sequence Domain Adaptation for Scene Text Recognition

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Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications. Existing works mainly focus on learning a general model with a huge number of synthetic text images to recognize unconstrained scene texts, and have achieved substantial progress. However, these methods are not quite applicable in many real-world scenarios where 1) high recognition accuracy is required, while 2) labeled samples are lacked. To tackle this challenging problem, this paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation between the synthetic source domain (with many synthetic labeled samples) and a specific target domain (with only some or a few real labeled samples). This is done by simultaneously learning each character’s feature representation with an attention mech- anism and establishing the corresponding character-level latent subspace with adversarial learning. Our approach can maximize the character-level confusion between the source domain and the target domain, thus achieves the sequence-level adaptation with even a small number of labeled samples in the target domain. Extensive experiments on various datasets show that our method significantly outperforms the finetuning scheme, and obtains comparable performance to the state-of-the-art STR methods.

Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation

Hai Tran, Sumyeong Ahn, Taeyoung Lee, Yung Yi

Auto-TLDR; Unsupervised Domain Adaptation using Artificial Classes

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We study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and unlabeled data from the target domain. There exists an array of recent research based on the idea of extracting features that are not only invariant for both domains but also provide high discriminative power for the target domain. In this paper, we propose an idea of improving the discriminativeness: Adding an extra artificial class and training the model on the given data together with the GAN-generated samples of the new class. The trained model based on the new class samples is capable of extracting the features that are more discriminative by repositioning data of current classes in the target domain and therefore increasing the distances among the target clusters in the feature space. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T. We conduct various experiments for the standard data commonly used for the evaluation of unsupervised domain adaptations and demonstrate that our algorithm achieves the SOTA performance for many scenarios.

DAPC: Domain Adaptation People Counting Via Style-Level Transfer Learning and Scene-Aware Estimation

Na Jiang, Xingsen Wen, Zhiping Shi

Auto-TLDR; Domain Adaptation People counting via Style-Level Transfer Learning and Scene-Aware Estimation

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People counting concentrates on predicting the number of people in surveillance images. It remains challenging due to the rich variations in scene type and crowd density. Besides, the limited closed-set with ground truth from reality significantly increase the difficulty of people counting in actual open-set. Targeting to solve these problems, this paper proposes a domain adaptation people counting via style-level transfer learning (STL) and scene-aware estimation (SAE). The style-level transfer learning explicitly leverages the style constraint and content similarity between images to learn effective knowledge transfer, which narrows the gap between closed-set and open-set by generating domain adaptation images. The scene-aware estimation introduces scene classifier to provide scene-aware weights for adaptively fusing density maps, which alleviates interference of variations in scene type and crowd density on domain adaptation people counting. Extensive experimental results demonstrate that images generated by STL are more suitable for domain adaptation learning and our proposed approach significantly outperforms the state-of-the-art methods on multiple cross-domain pairs.

A Simple Domain Shifting Network for Generating Low Quality Images

Guruprasad Hegde, Avinash Nittur Ramesh, Kanchana Vaishnavi Gandikota, Michael Möller, Roman Obermaisser

Auto-TLDR; Robotic Image Classification Using Quality degrading networks

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Deep Learning systems have proven to be extremely successful for image recognition tasks for which significant amounts of training data is available, e.g., on the famous ImageNet dataset. We demonstrate that for robotics applications with cheap camera equipment, the low image quality, however, influences the classification accuracy, and freely available data bases cannot be exploited in a straight forward way to train classifiers to be used on a robot. As a solution we propose to train a network on degrading the quality images in order to mimic specific low quality imaging systems. Numerical experiments demonstrate that classification networks trained by using images produced by our quality degrading network along with the high quality images outperform classification networks trained only on high quality data when used on a real robot system, while being significantly easier to use than competing zero-shot domain adaptation techniques.

Adaptive Word Embedding Module for Semantic Reasoning in Large-Scale Detection

Yu Zhang, Xiaoyu Wu, Ruolin Zhu

Auto-TLDR; Adaptive Word Embedding Module for Object Detection

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In recent years, convolutional neural networks have achieved rapid development in the field of object detection. However, due to the imbalance of data, high costs in labor and uneven level of data labeling, the overall performance of the previous detection network has dropped sharply when dataset extended to the large-scale with hundreds and thousands categories. We present the Adaptive Word Embedding Module, extracting the adaptive semantic knowledge graph to reach semantic consistency within one image. Our method endows the ability to infer global semantic of detection networks without other attribute or relationship annotations. Compared with Faster RCNN, the algorithm on the MSCOCO dataset was significantly improved by 4.1%, and the mAP value has reached 32.8%. On the VG1000 dataset, it increased by 0.9% to 6.7% compared with Faster RCNN. Adaptive Word Embedding Module is lightweight, general-purpose and can be plugged into diverse detection networks. Code will be made available.

Graph-Based Interpolation of Feature Vectors for Accurate Few-Shot Classification

Yuqing Hu, Vincent Gripon, Stéphane Pateux

Auto-TLDR; Transductive Learning for Few-Shot Classification using Graph Neural Networks

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In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples. In this context, works have proposed to introduce Graph Neural Networks (GNNs) aiming at exploiting the information contained in other samples treated concurrently, what is commonly referred to as the transductive setting in the literature. These GNNs are trained all together with a backbone feature extractor. In this paper, we propose a new method that relies on graphs only to interpolate feature vectors instead, resulting in a transductive learning setting with no additional parameters to train. Our proposed method thus exploits two levels of information: a) transfer features obtained on generic datasets, b) transductive information obtained from other samples to be classified. Using standard few-shot vision classification datasets, we demonstrate its ability to bring significant gains compared to other works.

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

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

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.

Shape Consistent 2D Keypoint Estimation under Domain Shift

Levi Vasconcelos, Massimiliano Mancini, Davide Boscaini, Barbara Caputo, Elisa Ricci

Auto-TLDR; Deep Adaptation for Keypoint Prediction under Domain Shift

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Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e.g. semantic segmentation, depth estimation). Following this trend, in this paper we present a novel deep adaptation framework for estimating keypoints under \textit{domain shift}, i.e. when the training (\textit{source}) and the test (\textit{target}) images significantly differ in terms of visual appearance. Our method seamlessly combines three different components: feature alignment, adversarial training and self-supervision. Specifically, our deep architecture leverages from domain-specific distribution alignment layers to perform target adaptation at the feature level. Furthermore, a novel loss is proposed which combines an adversarial term for ensuring aligned predictions in the output space and a geometric consistency term which guarantees coherent predictions between a target sample and its perturbed version. Our extensive experimental evaluation conducted on three publicly available benchmarks shows that our approach outperforms state-of-the-art domain adaptation methods in the 2D keypoint prediction task.

Progressive Unsupervised Domain Adaptation for Image-Based Person Re-Identification

Mingliang Yang, Da Huang, Jing Zhao

Auto-TLDR; Progressive Unsupervised Domain Adaptation for Person Re-Identification

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Unsupervised domain adaptation (UDA) has emerged as an effective paradigm for reducing the huge manual annotation cost for Person Re-Identification (Re-ID). Many of the recent UDA methods for Re-ID are clustering-based and select all the pseudo-label samples in each iteration for the model training. However, there are many wrong labeled samples that will mislead the model optimization under this circumstance. To solve this problem, we propose a Progressive Unsupervised Domain Adaptation (PUDA) framework for image-based Person Re-ID to reduce the negative effect of wrong pseudo-label samples on the model training process. Specifically, we first pretrain a CNN model on a labeled source dataset, then finetune the model on unlabeled target dataset with the following three steps iteratively: 1) estimating pseudo-labels for all the images in the target dataset with the model trained in the last iteration; 2) extending the training set by adding pseudo-label samples with higher label confidence; 3) updating the CNN model with the expanded training set in a supervised manner. During the iteration process, the number of pseudo-label samples added increased progressively. In particular, a Moderate Initial Selections (MIS) strategy for pseudo-label sampling is also proposed to reduce the negative impacts of random noise features in the early iterations and mislabeled samples in the late iterations on the model. The proposed framework with MIS strategy is validated on the Duke-to-Market, Market-to-Duke unsupervised domain adaptation tasks and achieves improvements of 4.2 points (absolute, i.e., 80.0% vs. 75.8%) and 1.7 points (absolute, i.e., 70.7% vs. 69.0%) in mAP correspondingly.

Learning Low-Shot Generative Networks for Cross-Domain Data

Hsuan-Kai Kao, Cheng-Che Lee, Wei-Chen Chiu

Auto-TLDR; Learning Generators for Cross-Domain Data under Low-Shot Learning

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We tackle a novel problem of learning generators for cross-domain data under a specific scenario of low-shot learning. Basically, given a source domain with sufficient amount of training data, we aim to transfer the knowledge of its generative process to another target domain, which not only has few data samples but also contains the domain shift with respect to the source domain. This problem has great potential in practical use and is different from the well-known image translation task, as the target-domain data can be generated without requiring any source-domain ones and the large data consumption for learning target-domain generator can be alleviated. Built upon a cross-domain dataset where (1) each of the low shots in the target domain has its correspondence in the source and (2) these two domains share the similar content information but different appearance, two approaches are proposed: a Latent-Disentanglement-Orientated model (LaDo) and a Generative-Hierarchy-Oriented (GenHo) model. Our LaDo and GenHo approaches address the problem from different perspectives, where the former relies on learning the disentangled representation composed of domain-invariant content features and domain-specific appearance ones; while the later decomposes the generative process of a generator into two parts for synthesizing the content and appearance sequentially. We perform extensive experiments under various settings of cross-domain data and show the efficacy of our models for generating target-domain data with the abundant content variance as in the source domain, which lead to the favourable performance in comparison to several baselines.

DAIL: Dataset-Aware and Invariant Learning for Face Recognition

Gaoang Wang, Chen Lin, Tianqiang Liu, Mingwei He, Jiebo Luo

Auto-TLDR; DAIL: Dataset-Aware and Invariant Learning for Face Recognition

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To achieve good performance in face recognition, a large scale training dataset is usually required. A simple yet effective way for improving the recognition performance is to use a dataset as large as possible by combining multiple datasets in the training. However, it is problematic and troublesome to naively combine different datasets due to two major issues. Firstly, the same person can possibly appear in different datasets, leading to the identity overlapping issue between different datasets. Natively treating the same person as different classes in different datasets during training will affect back-propagation and generate non-representative embeddings. On the other hand, manually cleaning labels will take a lot of human efforts, especially when there are millions of images and thousands of identities. Secondly, different datasets are collected in different situations and thus will lead to different domain distributions. Natively combining datasets will lead to domain distribution differences and make it difficult to learn domain invariant embeddings across different datasets. In this paper, we propose DAIL: Dataset-Aware and Invariant Learning to resolve the above-mentioned issues. To solve the first issue of identity overlapping, we propose a dataset-aware loss for multi-dataset training by reducing the penalty when the same person appears in multiple datasets. This can be readily achieved with a modified softmax loss with a dataset-aware term. To solve the second issue, the domain adaptation with gradient reversal layers is employed for dataset invariant learning. The proposed approach not only achieves state-of-the-art results on several commonly used face recognition validation sets, like LFW, CFP-FP, AgeDB-30, but also shows great benefit for practical usage.

Label Incorporated Graph Neural Networks for Text Classification

Yuan Xin, Linli Xu, Junliang Guo, Jiquan Li, Xin Sheng, Yuanyuan Zhou

Auto-TLDR; Graph Neural Networks for Semi-supervised Text Classification

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Graph Neural Networks (GNNs) have achieved great success on graph-structured data, and their applications on traditional data structures such as natural language processing and semi-supervised text classification have been extensively explored in recent years. While previous works only consider the text information while building the graph, heterogeneous information such as labels is ignored. In this paper, we consider to incorporate the label information while building the graph by adding text-label-text paths, through which the supervision information will propagate among the graph more directly. Specifically, we treat labels as nodes in the graph which also contains text and word nodes, and then connect labels with texts belonging to that label. Through graph convolutions, label embeddings are jointly learned with text embeddings in the same latent semantic space. The newly incorporated label nodes will facilitate learning more accurate text embeddings by introducing the label information, and thus benefit the downstream text classification tasks. Extensive results on several benchmark datasets show that the proposed framework outperforms baseline methods by a significant margin.

Domain Generalized Person Re-Identification Via Cross-Domain Episodic Learning

Ci-Siang Lin, Yuan Chia Cheng, Yu-Chiang Frank Wang

Auto-TLDR; Domain-Invariant Person Re-identification with Episodic Learning

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Aiming at recognizing images of the same person across distinct camera views, person re-identification (re-ID) has been among active research topics in computer vision. Most existing re-ID works require collection of a large amount of labeled image data from the scenes of interest. When the data to be recognized are different from the source-domain training ones, a number of domain adaptation approaches have been proposed. Nevertheless, one still needs to collect labeled or unlabelled target-domain data during training. In this paper, we tackle an even more challenging and practical setting, domain generalized (DG) person re-ID. That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data. In order to learn domain-invariant features without knowing the target domain of interest, we present an episodic learning scheme which advances meta learning strategies to exploit the observed source-domain labeled data. The learned features would exhibit sufficient domain-invariant properties while not overfitting the source-domain data or ID labels. Our experiments on four benchmark datasets confirm the superiority of our method over the state-of-the-arts.

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

Yaning Li, Liu Yang

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.

Context for Object Detection Via Lightweight Global and Mid-Level Representations

Mesut Erhan Unal, Adriana Kovashka

Auto-TLDR; Context-Based Object Detection with Semantic Similarity

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We propose an approach for explicitly capturing context in object detection. We model visual and geometric relationships between object regions, but also model the global scene as a first-class participant. In contrast to prior approaches, both the context we rely on, as well as our proposed mechanism for belief propagation over regions, is lightweight. We also experiment with capturing similarities between regions at a semantic level, by modeling class co-occurrence and linguistic similarity between class names. We show that our approach significantly outperforms Faster R-CNN, and performs competitively with a much more costly approach that also models context.

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

Jinting Wu, Yujia Zhang, Xiao-Guang Zhao

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.

Manual-Label Free 3D Detection Via an Open-Source Simulator

Zhen Yang, Chi Zhang, Zhaoxiang Zhang, Huiming Guo

Auto-TLDR; DA-VoxelNet: A Novel Domain Adaptive VoxelNet for LIDAR-based 3D Object Detection

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LiDAR based 3D object detectors typically need a large amount of detailed-labeled point cloud data for training, but these detailed labels are commonly expensive to acquire. In this paper, we propose a manual-label free 3D detection algorithm that leverages the CARLA simulator to generate a large amount of self-labeled training samples and introduces a novel Domain Adaptive VoxelNet (DA-VoxelNet) that can cross the distribution gap from the synthetic data to the real scenario. The self-labeled training samples are generated by a set of high quality 3D models embedded in a CARLA simulator and a proposed LiDAR-guided sampling algorithm. Then a DA-VoxelNet that integrates both a sample-level DA module and an anchor-level DA module is proposed to enable the detector trained by the synthetic data to adapt to real scenario. Experimental results show that the proposed unsupervised DA 3D detector on KITTI evaluation set can achieve 76.66% and 56.64% mAP on BEV mode and 3D mode respectively. The results reveal a promising perspective of training a LIDAR-based 3D detector without any hand-tagged label.

Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling

Fabian Dubourvieux, Romaric Audigier, Angélique Loesch, Ainouz-Zemouche Samia, Stéphane Canu

Auto-TLDR; Pseudo-labeling for Unsupervised Domain Adaptation for Person Re-Identification

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Person Re-Identification (re-ID) aims at retrieving images of the same person taken by different cameras. A challenge for re-ID is the performance preservation when a model is used on data of interest (target data) which belong to a different domain from the training data domain (source data). Unsupervised Domain Adaptation (UDA) is an interesting research direction for this challenge as it avoids a costly annotation of the target data. Pseudo-labeling methods achieve the best results in UDA-based re-ID. They incrementally learn with identity pseudo-labels which are initialized by clustering features in the source re-ID encoder space. Surprisingly, labeled source data are discarded after this initialization step. However, we believe that pseudo-labeling could further leverage the labeled source data in order to improve the post-initialization training steps. In order to improve robustness against erroneous pseudo-labels, we advocate the exploitation of both labeled source data and pseudo-labeled target data during all training iterations. To support our guideline, we introduce a framework which relies on a two-branch architecture optimizing classification in source and target domains, respectively, in order to allow adaptability to the target domain while ensuring robustness to noisy pseudo-labels. Indeed, shared low and mid-level parameters benefit from the source classification signal while high-level parameters of the target branch learn domain-specific features. Our method is simple enough to be easily combined with existing pseudo-labeling UDA approaches. We show experimentally that it is efficient and improves performance when the base method has no mechanism to deal with pseudo-label noise. And it maintains performance when combined with base method that already manages pseudo-label noise. Our approach reaches state-of-the-art performance when evaluated on commonly used datasets, Market-1501 and DukeMTMC-reID, and outperforms the state of the art when targeting the bigger and more challenging dataset MSMT.

TAAN: Task-Aware Attention Network for Few-Shot Classification

Zhe Wang, Li Liu, Fanzhang Li

Auto-TLDR; TAAN: Task-Aware Attention Network for Few-Shot Classification

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Few-shot classification aims to recognize unlabeled samples from unseen classes given only a few labeled samples.Current approaches of few-shot learning usually employ a metriclearning framework to learn a feature similarity comparison between a query (test) example and the few support (training) examples. However, these approaches all extract features from samples independently without looking at the entire task as a whole, and so fail to provide an enough discrimination to features. Moreover, the existing approaches lack the ability to select the most relevant features for the task at hand. In this work, we propose a novel algorithm called Task-Aware Attention Network (TAAN) to address the above problems in few-shot classification. By inserting a Task-Relevant Channel Attention Module into metric-based few-shot learners, TAAN generates channel attentions for each sample by aggregating the context of the entire support set and identifies the most relevant features for similarity comparison. The experiment demonstrates that TAAN is competitive in overall performance comparing to the recent state-of-the-art systems and improves the performance considerably over baseline systems on both mini-ImageNet and tiered-ImageNet benchmarks.

AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network

Yuhang Zhang, Hongshuai Ren, Jiexia Ye, Xitong Gao, Yang Wang, Kejiang Ye, Cheng-Zhong Xu

Auto-TLDR; Adjacency Matrix for Graph Convolutional Network in Non-Euclidean Space

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Graph Convolutional Network (GCN) is adopted to tackle the problem of the convolution operation in non-Euclidean space. Although previous works on GCN have made some progress, one of their limitations is that their input Adjacency Matrix (AM) is designed manually and requires domain knowledge, which is cumbersome, tedious and error-prone. In addition, entries of this fixed Adjacency Matrix are generally designed as binary values (i.e., ones and zeros) which can not reflect more complex relationship between nodes. However, many applications require a weighted and dynamic Adjacency Matrix instead of an unweighted and fixed Adjacency Matrix. To this end, there are few works focusing on designing a more flexible Adjacency Matrix. In this paper, we propose an end-to-end algorithm to improve the GCN performance by focusing on the Adjacency Matrix. We first provide a calculation method that called node information entropy to update the matrix. Then, we analyze the search strategy in a continuous space and introduce the Deep Deterministic Policy Gradient (DDPG) method to overcome the demerit of the discrete space search. Finally, we integrate the GCN and reinforcement learning into an end-to-end framework. Our method can automatically define the adjacency matrix without artificial knowledge. At the same time, the proposed approach can deal with any size of the matrix and provide a better value for the network. Four popular datasets are selected to evaluate the capability of our algorithm. The method in this paper achieves the state-of-the-art performance on Cora and Pubmed datasets, respectively, with the accuracy of 84.6% and 81.6%.

Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training

Teo Spadotto, Marco Toldo, Umberto Michieli, Pietro Zanuttigh

Auto-TLDR; Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes

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Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled data is available. In this paper, we consider the semantic segmentation of urban scenes and we propose an approach to adapt a deep neural network trained on synthetic data to real scenes addressing the domain shift between the two different data distributions. We introduce a novel UDA framework where a standard supervised loss on labeled synthetic data is supported by an adversarial module and a self-training strategy aiming at aligning the two domain distributions. The adversarial module is driven by a couple of fully convolutional discriminators dealing with different domains: the first discriminates between ground truth and generated maps, while the second between segmentation maps coming from synthetic or real world data. The self-training module exploits the confidence estimated by the discriminators on unlabeled data to select the regions used to reinforce the learning process. Furthermore, the confidence is thresholded with an adaptive mechanism based on the per-class overall confidence. Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets like GTA5 and SYNTHIA, to real world datasets like Cityscapes and Mapillary.

Exploiting Knowledge Embedded Soft Labels for Image Recognition

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

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.

Foreground-Focused Domain Adaption for Object Detection

Yuchen Yang, Nilanjan Ray

Auto-TLDR; Unsupervised Domain Adaptation for Unsupervised Object Detection

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Object detectors suffer from accuracy loss caused by domain shift from a source to a target domain. Unsupervised domain adaptation (UDA) approaches mitigate this loss by training with unlabeled target domain images. A popular processing pipeline applies adversarial training that aligns the distributions of the features from the two domains. We advocate that aligning the full image level features is not ideal for UDA object detection due to the presence of varied background areas during inference. Thus, we propose a novel foreground-focused domain adaptation (FFDA) framework which mines the loss of the domain discriminators to concentrate on the backpropagation of foreground loss. We obtain mining masks by collecting target predictions and source labels to outline foreground regions, and apply the masks to image and instance level domain discriminators to allow backpropagation only on the mined regions. By reinforcing this foreground-focused adaptation throughout multiple layers in the detector model, we gain a significant accuracy boost on the target domain prediction. Compared to previous works, our method reaches the new state-of-the-art accuracy on adapting Cityscape to Foggy Cityscape dataset and demonstrates competitive accuracy on other datasets that include various scenarios for autonomous driving applications.

Self-Supervised Domain Adaptation with Consistency Training

Liang Xiao, Jiaolong Xu, Dawei Zhao, Zhiyu Wang, Li Wang, Yiming Nie, Bin Dai

Auto-TLDR; Unsupervised Domain Adaptation for Image Classification

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We consider the problem of unsupervised domain adaptation for image classification. To learn target-domain-aware features from the unlabeled data, we create a self-supervised pretext task by augmenting the unlabeled data with a certain type of transformation (specifically, image rotation) and ask the learner to predict the properties of the transformation. However, the obtained feature representation may contain a large amount of irrelevant information with respect to the main task. To provide further guidance, we force the feature representation of the augmented data to be consistent with that of the original data. Intuitively, the consistency introduces additional constraints to representation learning, therefore, the learned representation is more likely to focus on the right information about the main task. Our experimental results validate the proposed method and demonstrate state-of-the-art performance on classical domain adaptation benchmarks.

Respecting Domain Relations: Hypothesis Invariance for Domain Generalization

Ziqi Wang, Marco Loog, Jan Van Gemert

Auto-TLDR; Learning Hypothesis Invariant Representations for Domain Generalization

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In domain generalization, multiple labeled non-independent and non-identically distributed source domains are available during training while neither the data nor the labels of target domains are. Currently, learning so-called domain invariant representations (DIRs) is the prevalent approach to domain generalization. In this work, we define DIRs employed by existing works in probabilistic terms and show that by learning DIRs, overly strict requirements are imposed concerning the invariance. Particularly, DIRs aim to perfectly align representations of different domains, i.e. their input distributions. This is, however, not necessary for good generalization to a target domain and may even dispose of valuable classification information. We propose to learn so-called hypothesis invariant representations (HIRs), which relax the invariance assumptions. We report experimental results on public domain generalization datasets to show that learning HIRs is more effective than learning DIRs. In fact, our approach can even compete with approaches using prior knowledge about domains.

GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks

Edward Collier, Supratik Mukhopadhyay

Auto-TLDR; Approximating Adversarial Learning in Deep Neural Networks Using Set and Class Adversaries

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Recent work in deep neural networks has sought to characterize the nature in which a network learns features and how applicable learnt features are to various problem sets. Deep neural network applicability can be split into three sub-problems; set applicability, class applicability, and instance applicability. In this work we seek to quantify the applicability of features learned during adversarial training, focusing specifically on set and class applicability. We apply techniques for measuring applicability to both generators and discriminators trained on various data sets to quantify applicability and better observe how both a generator and a discriminator, and generative models as a whole, learn features during adversarial training.

Energy-Constrained Self-Training for Unsupervised Domain Adaptation

Xiaofeng Liu, Xiongchang Liu, Bo Hu, Jun Lu, Jonghye Woo, Jane You

Auto-TLDR; Unsupervised Domain Adaptation with Energy Function Minimization

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Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain. Recently, the deep self-training involves an iterative process of predicting on the target domain and then taking the confident predictions as hard pseudo-labels for retraining. However, the pseudo-labels are usually unreliable, and easily leading to deviated solutions with propagated errors. In this paper, we resort to the energy-based model and constrain the training of the unlabeled target sample with the energy function minimization objective. It can be applied as a simple additional regularization. In this framework, it is possible to gain the benefits of the energy-based model, while retaining strong discriminative performance following a plug-and-play fashion. The convergence property and its connection with classification expectation minimization are investigated. We deliver extensive experiments on the most popular and large scale UDA benchmarks of image classification as well as semantic segmentation to demonstrate its generality and effectiveness.

Parallel Network to Learn Novelty from the Known

Shuaiyuan Du, Chaoyi Hong, Zhiyu Pan, Chen Feng, Zhiguo Cao

Auto-TLDR; Trainable Parallel Network for Pseudo-Novel Detection

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Towards multi-class novelty detection, we propose an end-to-end trainable Parallel Network (PN) using no additional data but only the training set itself. Our key idea is to first divide the training set into successive subtasks of pseudo-novelty detection to simulate real scenarios. We then design a multi-branch PN to well address the fine-grained division, which yields a compressed and more discriminative classification space and forms a natural ensemble. In practice, we divide the training data into subsets consisting of known and pseudo-novel classes. Each subset forms a sub-task fed to one branch in PN. During training, both known and pseudo-novel classes are uniformly distributed over the branches for better data balance and model diversity. By distinguishing between the known and the diverse pseudo-novel, PN extracts the concept of novelty in a compressed classification space. This provides PN with generalization ability to real novel classes which are absent during training. During online inference, this ability is further strengthened with the ensemble of PN's multiple branches. Experiments on three public datasets show our method's superiority to the mainstream methods.

Joint Supervised and Self-Supervised Learning for 3D Real World Challenges

Antonio Alliegro, Davide Boscaini, Tatiana Tommasi

Auto-TLDR; Self-supervision for 3D Shape Classification and Segmentation in Point Clouds

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Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world. In many practical conditions the amount of annotated data may be limited and integrating new sources of knowledge becomes crucial to support autonomous learning. Here we consider several scenarios involving synthetic and real world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation. An extensive analysis investigating few-shot, transfer learning and cross-domain settings shows the effectiveness of our approach with state-of-the-art results for 3D shape classification and part segmentation.

GCNs-Based Context-Aware Short Text Similarity Model

Xiaoqi Sun

Auto-TLDR; Context-Aware Graph Convolutional Network for Text Similarity

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Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which cannot capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extend the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

Sketch-SNet: Deeper Subdivision of Temporal Cues for Sketch Recognition

Yizhou Tan, Lan Yang, Honggang Zhang

Auto-TLDR; Sketch Recognition using Invariable Structural Feature and Drawing Habits Feature

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Sketch recognition is a central task in sketchrelated researches. Different from the natural image, the sparse pixel distribution of sketch destroys the visual texture which encourages researchers to explore the temporal information of sketch. With the release of million-scale datasets, we explore the invariable structure of sketch and specific order of strokes in sketch. Prior works based on Recurrent Neural Network (RNN) trend to output different features with changed stroke orders. In particular, we adopt a novel method by employing a Graph Convolutional Network (GCN) to extract invariable structural feature under any orders of strokes. Compared to traditional comprehension of sketch, we further split the temporal information of sketch into two types of feature (invariable structural feature (ISF) and drawing habits feature (DHF)) which aim to reduce the confusion in temporal information. We propose a two-branch GCN-RNN network to extract two types of feature respectively, termed Sketch-SNet. The GCN branch is encouraged to extract the ISF through receiving various shuffled strokes of an input sketch. The RNN branch takes the original input to extract DHF by learning the pattern of strokes’ order. Meanwhile, we introduce semantic information to generate soft-labels owing to the high abstractness of sketch. Extensive experiments on the Quick-Draw dataset demonstrate that our further subdivision of temporal information improves the performance of sketch recognition which surpasses state-of-the-art by a large margin.

Multi-Label Contrastive Focal Loss for Pedestrian Attribute Recognition

Xiaoqiang Zheng, Zhenxia Yu, Lin Chen, Fan Zhu, Shilong Wang

Auto-TLDR; Multi-label Contrastive Focal Loss for Pedestrian Attribute Recognition

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Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep constitutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in comparison.

Reinforcement Learning with Dual Attention Guided Graph Convolution for Relation Extraction

Zhixin Li, Yaru Sun, Suqin Tang, Canlong Zhang, Huifang Ma

Auto-TLDR; Dual Attention Graph Convolutional Network for Relation Extraction

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To better learn the dependency relationship between nodes, we address the relationship extraction task by capturing rich contextual dependencies based on the attention mechanism, and using distributional reinforcement learning to generate optimal relation information representation. This method is called Dual Attention Graph Convolutional Network (DAGCN), to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of GCN, which model the semantic interdependencies in spatial and relational dimensions respectively. The position attention module selectively aggregates the feature at each position by a weighted sum of the features at all positions of nodes internal features. Meanwhile, the relation attention module selectively emphasizes interdependent node relations by integrating associated features among all nodes. We sum the outputs of the two attention modules and use reinforcement learning to predict the classification of nodes relationship to further improve feature representation which contributes to more precise extraction results. The results on the TACRED and SemEval datasets show that the model can obtain more useful information for relational extraction tasks, and achieve better performances on various evaluation indexes.

Rethinking Domain Generalization Baselines

Francesco Cappio Borlino, Antonio D'Innocente, Tatiana Tommasi

Auto-TLDR; Style Transfer Data Augmentation for Domain Generalization

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Despite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained. Domain generalization methods investigate this problem and data augmentation strategies have shown to be helpful tools to increase data variability, supporting model robustness across domains. In our work we focus on style transfer data augmentation and we present how it can be implemented with a simple and inexpensive strategy to improve generalization. Moreover, we analyze the behavior of current state of the art domain generalization methods when integrated with this augmentation solution: our thorough experimental evaluation shows that their original effect almost always disappears with respect to the augmented baseline. This issue open new scenarios for domain generalization research, highlighting the need of novel methods properly able to take advantage of the introduced data variability.

Boundary-Aware Graph Convolution for Semantic Segmentation

Hanzhe Hu, Jinshi Cui, Jinshi Hongbin Zha

Auto-TLDR; Boundary-Aware Graph Convolution for Semantic Segmentation

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Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution Network(BGCNet), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.