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

Hong Liu, Ziling Miao, Bing Yang, Runwei Ding

Responsive image

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

Slides Poster

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.

Similar papers

RGB-Infrared Person Re-Identification Via Image Modality Conversion

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

Responsive image

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

Slides Poster Similar

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.

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

Can Zhang, Hong Liu, Wei Guo, Mang Ye

Responsive image

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

Slides Poster Similar

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.

Polynomial Universal Adversarial Perturbations for Person Re-Identification

Wenjie Ding, Xing Wei, Rongrong Ji, Xiaopeng Hong, Yihong Gong

Responsive image

Auto-TLDR; Polynomial Universal Adversarial Perturbation for Re-identification Methods

Slides Poster Similar

In this paper, we focus on Universal Adversarial Perturbations (UAP) attack on state-of-the-art person re-identification (Re-ID) methods. Existing UAP methods usually compute a perturbation image and add it to the images of interest. Such a simple constant form greatly limits the attack power. To address this problem, we extend the formulation of UAP to a polynomial form and propose the Polynomial Universal Adversarial Perturbation (PUAP). Unlike traditional UAP methods which only rely on the additive perturbation signal, the proposed PUAP consists of both an additive perturbation and a multiplicative modulation factor. The additive perturbation produces the fundamental component of the signal, while the multiplicative factor modulates the perturbation signal in line with the unit impulse pattern of the input image. Moreover, we design a Pearson correlation coefficient loss to generate universal perturbations, for disrupting the outputs of person Re-ID methods. Extensive experiments on DukeMTMC-ReID, Market-1501, and MARS show that the proposed method can efficiently improve the attack performance, especially when the magnitude of UAP is constrained to a small value.

Attentive Part-Aware Networks for Partial Person Re-Identification

Lijuan Huo, Chunfeng Song, Zhengyi Liu, Zhaoxiang Zhang

Responsive image

Auto-TLDR; Part-Aware Learning for Partial Person Re-identification

Slides Poster Similar

Partial person re-identification (re-ID) refers to re-identify a person through occluded images. It suffers from two major challenges, i.e., insufficient training data and incomplete probe image. In this paper, we introduce an automatic data augmentation module and a part-aware learning method for partial re-identification. On the one hand, we adopt the data augmentation to enhance the training data and help learns more stabler partial features. On the other hand, we intuitively find that the partial person images usually have fixed percentages of parts, therefore, in partial person re-id task, the probe image could be cropped from the pictures and divided into several different partial types following fixed ratios. Based on the cropped images, we propose the Cropping Type Consistency (CTC) loss to classify the cropping types of partial images. Moreover, in order to help the network better fit the generated and cropped data, we incorporate the Block Attention Mechanism (BAM) into the framework for attentive learning. To enhance the retrieval performance in the inference stage, we implement cropping on gallery images according to the predicted types of probe partial images. Through calculating feature distances between the partial image and the cropped holistic gallery images, we can recognize the right person from the gallery. To validate the effectiveness of our approach, we conduct extensive experiments on the partial re-ID benchmarks and achieve state-of-the-art performance.

Self and Channel Attention Network for Person Re-Identification

Asad Munir, Niki Martinel, Christian Micheloni

Responsive image

Auto-TLDR; SCAN: Self and Channel Attention Network for Person Re-identification

Slides Poster Similar

Recent research has shown promising results for person re-identification by focusing on several trends. One is designing efficient metric learning loss functions such as triplet loss family to learn the most discriminative representations. The other is learning local features by designing part based architectures to form an informative descriptor from semantically coherent parts. Some efforts adjust distant outliers to their most similar positions by using soft attention and learn the relationship between distant similar features. However, only a few prior efforts focus on channel-wise dependencies and learn non-local sharp similar part features directly for the degraded data in the person re-identification task. In this paper, we propose a novel Self and Channel Attention Network (SCAN) to model long-range dependencies between channels and feature maps. We add multiple classifiers to learn discriminative global features by using classification loss. Self Attention (SA) module and Channel Attention (CA) module are introduced to model non-local and channel-wise dependencies in the learned features. Spectral normalization is applied to the whole network to stabilize the training process. Experimental results on the person re-identification benchmarks show the proposed components achieve significant improvement with respect to the baseline.

Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification

Rodolfo Quispe, Helio Pedrini

Responsive image

Auto-TLDR; Top-DB-Net for Person Re-Identification using Top DropBlock

Slides Poster Similar

Person Re-Identification is a challenging task that aims to retrieve all instances of a query image across a system of non-overlapping cameras. Due to the various extreme changes of view, it is common that local regions that could be used to match people are suppressed, which leads to a scenario where approaches have to evaluate the similarity of images based on less informative regions. In this work, we introduce the Top-DB-Net, a method based on Top DropBlock that pushes the network to learn to focus on the scene foreground, with special emphasis on the most task-relevant regions and, at the same time, encodes low informative regions to provide high discriminability. The Top-DB-Net is composed of three streams: (i) a global stream encodes rich image information from a backbone, (ii) the Top DropBlock stream encourages the backbone to encode low informative regions with high discriminative features, and (iii) a regularization stream helps to deal with the noise created by the dropping process of the second stream, when testing the first two streams are used. Vast experiments on three challenging datasets show the capabilities of our approach against state-of-the-art methods. Qualitative results demonstrate that our method exhibits better activation maps focusing on reliable parts of the input images.

Rethinking ReID：Multi-Feature Fusion Person Re-Identification Based on Orientation Constraints

Mingjing Ai, Guozhi Shan, Bo Liu, Tianyang Liu

Responsive image

Auto-TLDR; Person Re-identification with Orientation Constrained Network

Slides Poster Similar

Person re-identification (ReID) aims to identify the specific pedestrian in a series of images or videos. Recently, ReID is receiving more and more attention in the fields of computer vision research and application like intelligent security. One major issue downgrading the ReID model performance lies in that various subjects in the same body orientations look too similar to distinguish by the model, while the same subject viewed in different orientations looks rather different. However, most of the current studies do not particularly differentiate pedestrians in orientation when designing the network, so we rethink this problem particularly from the perspective of person orientation and propose a new network structure by including two branches: one handling samples with the same body orientations and the other handling samples with different body orientations. Correspondingly, we also propose an orientation classifier that can accurately distinguish the orientation of each person. At the same time, the three-part loss functions are introduced for orientation constraint and combined to optimize the network simultaneously. Also, we use global and local features int the training stage in order to make use of multi-level information. Therefore, our network can derive its efficacy from orientation constraints and multiple features. Experiments show that our method not only has competitive performance on multiple datasets, but also can let retrieval results aligned with the orientation of the query sample rank higher, which may have great potential in the practical applications.

Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning

Vladislav Sovrasov, Dmitry Sidnev

Responsive image

Auto-TLDR; Cross-Domain Generalization in Person Re-identification using Omni-Scale Network

Slides Similar

This work considers the problem of domain shift in person re-identification.Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, namely AM-Softmax loss, and some additional training practices to build well-generalizing, yet, computationally efficient models. We use recently proposed Omni-Scale Network (OSNet) architecture combined with several training tricks and architecture adjustments to obtain state-of-the art results in cross-domain generalization problem on a large-scale MSMT17 dataset in three setups: MSMT17-all->DukeMTMC, MSMT17-train->Market1501 and MSMT17-all->Market1501.

Pose Variation Adaptation for Person Re-Identification

Lei Zhang, Na Jiang, Qishuai Diao, Yue Xu, Zhong Zhou, Wei Wu

Responsive image

Auto-TLDR; Pose Transfer Generative Adversarial Network for Person Re-identification

Slides Poster Similar

Person re-identification (reid) plays an important role in surveillance video analysis, especially for criminal investigation and intelligent security. Although a large number of effective feature or distance metric learning approaches have been proposed, it still suffers from pedestrians appearance variations caused by pose changing. Most of the previous methods address this problem by learning a pose-invariant descriptor subspace. In this paper, we propose a pose variation adaptation method for person reid in the view of data augmentation. It can reduce the probability of deep learning network over-fitting. Specifically, we introduce a pose transfer generative adversarial network with a similarity measurement constraint. With the learned pose transfer model, training images can be pose-transferred to any given poses, and along with the original images, form a augmented training dataset. It increases data diversity against over-fitting. In contrast to previous GAN-based methods, we consider the influence of pose variations on similarity measure to generate more realistic and shaper samples for person reid. Besides, we optimize hard example mining to introduce a novel manner of samples (pose-transferred images) used with the learned pose transfer model. It focuses on the inferior samples which are caused by pose variations to increase the number of effective hard examples for learning discriminative features and improve the generalization ability. We extensively conduct comparative evaluations to demonstrate the advantages and superiority of our proposed method over the state-of-the-art approaches on Market-1501 and DukeMTMC-reID, the rank-1 accuracy is 96.1% for Market-1501 and 92.0% for DukeMTMC-reID.

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

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

Responsive image

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

Slides Poster Similar

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.

Cross-spectrum Face Recognition Using Subspace Projection Hashing

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

Responsive image

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

Slides Poster Similar

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.

Deep Top-Rank Counter Metric for Person Re-Identification

Chen Chen, Hao Dou, Xiyuan Hu, Silong Peng

Responsive image

Auto-TLDR; Deep Top-Rank Counter Metric for Person Re-identification

Slides Poster Similar

In the research field of person re-identification, deep metric learning that guides the efficient and effective embedding learning serves as one of the most fundamental tasks. Recent efforts of the loss function based deep metric learning methods mainly focus on the top rank accuracy optimization by minimiz- ing the distance difference between the correctly matching sample pair and wrongly matched sample pair. However, it is more straightforward to count the occurrences of correct top-rank candidates and maximize the counting results for better top rank accuracy. In this paper, we propose a generalized logistic function based metric with effective practicalness in deep learning, namely the“deep top-rank counter metric”, to approximately optimize the counted occurrences of the correct top-rank matches. The properties that qualify the proposed metric as a well-suited deep re-identification metric have been discussed and a progressive hard sample mining strategy is also introduced for effective training and performance boosting. The extensive experiments show that the proposed top-rank counter metric outperforms other loss function based deep metrics and achieves the state-of- the-art accuracies.

Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-Identification

Toby Breckon, Aishah Alsehaim

Responsive image

Auto-TLDR; ResNet50-IBN for Video-based Person Re-Identification using Single Stream 2D Convolution Network

Slides Poster Similar

Video-based person re-identification has received increasing attention recently, as it plays an important role within the surveillance video analysis. Video-based Re-ID is an expansion of earlier image-based re-identification methods by learning features from a video via multiple image frames for each person. Most contemporary video Re-ID methods utilise complex CNN-based network architectures using 3D convolution or multi-branch networks to extract spatial-temporal features from the video. By contrast, in this paper, we will illustrate superior performance from a simple single stream 2D convolution network leveraging the ResNet50-IBN architecture to extract frame-level features followed by temporal attention for clip level features. These clip level features can be generalised to extract video level features by averaging clip level features without any additional cost. Our model, uses best video Re-ID practice and transfer learning between datasets, outperforms existing state-of-the-art approaches on MARS, PRID2011 and iLIDSVID datasets with 89:62%, 97:75%, 97:33% rank-1 accuracy respectively and with 84:61% mAP for MARS, without reliance on complex and memory intensive 3D convolutions or multistream networks architectures as found in other contemporary work. Conversely, this work shows that global features extracted by the 2D convolution network are a sufficient representation for robust state of the art video Re-ID.

Adaptive L2 Regularization in Person Re-Identification

Xingyang Ni, Liang Fang, Heikki Juhani Huttunen

Responsive image

Auto-TLDR; AdaptiveReID: Adaptive L2 Regularization for Person Re-identification

Slides Poster Similar

We introduce an adaptive L2 regularization mechanism termed AdaptiveReID, in the setting of person re-identification. In the literature, it is common practice to utilize hand-picked regularization factors which remain constant throughout the training procedure. Unlike existing approaches, the regularization factors in our proposed method are updated adaptively through backpropagation. This is achieved by incorporating trainable scalar variables as the regularization factors, which are further fed into a scaled hard sigmoid function. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets validate the effectiveness of our framework. Most notably, we obtain state-of-the-art performance on MSMT17, which is the largest dataset for person re-identification. Source code will be published at https://github.com/nixingyang/AdaptiveReID.

CANU-ReID: A Conditional Adversarial Network for Unsupervised Person Re-IDentification

Guillaume Delorme, Yihong Xu, Stéphane Lathuiliere, Radu Horaud, Xavier Alameda-Pineda

Responsive image

Auto-TLDR; Unsupervised Person Re-Identification with Clustering and Adversarial Learning

Slides Similar

Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pseudo-ID labels, and uses them to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired by domain adaptation, to match distributions from different domains. Since target data is distributed across different camera viewpoints, we propose to model each camera as an independent domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, we thus introduce a conditioning vector to mitigate this undesirable effect. In our framework, the centroid of the cluster to which the visual sample belongs is used as conditioning vector of our conditional adversarial network, where the vector is permutation invariant (clusters ordering does not matter) and its size is independent of the number of clusters. To our knowledge, we are the first to propose the use of conditional adversarial networks for unsupervised person re-ID. We evaluate the proposed architecture on top of two state-of-the-art clustering-based unsupervised person re-identification (re-ID) methods on four different experimental settings with three different data sets and set the new state-of-the-art performance on all four of them. Our code and model will be made publicly available at https://team.inria.fr/perception/canu-reid/.

Progressive Learning Algorithm for Efficient Person Re-Identification

Zhen Li, Hanyang Shao, Liang Niu, Nian Xue

Responsive image

Auto-TLDR; Progressive Learning Algorithm for Large-Scale Person Re-Identification

Slides Poster Similar

This paper studies the problem of Person Re-Identification (ReID) for large-scale applications. Recent research efforts have been devoted to building complicated part models, which introduce considerably high computational cost and memory consumption, inhibiting its practicability in large-scale applications. This paper aims to develop a novel learning strategy to find efficient feature embeddings while maintaining the balance of accuracy and model complexity. More specifically, we find by enhancing the classical triplet loss together with cross-entropy loss, our method can explore the hard examples and build a discriminant feature embedding yet compact enough for large-scale applications. Our method is carried out progressively using Bayesian optimization, and we call it the Progressive Learning Algorithm (PLA). Extensive experiments on three large-scale datasets show that our PLA is comparable or better than the state-of-the-arts. Especially, on the challenging Market-1501 dataset, we achieve Rank-1=94.7\%/mAP=89.4\% while saving at least 30\% parameters than strong part models.

Open-World Group Retrieval with Ambiguity Removal: A Benchmark

Ling Mei, Jian-Huang Lai, Zhanxiang Feng, Xiaohua Xie

Responsive image

Auto-TLDR; P2GSM-AR: Re-identifying changing groups of people under the open-world and group-ambiguity scenarios

Slides Poster Similar

Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. This paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. The open-world scenario considers that there are probably non-target people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. The open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.

Attention-Based Model with Attribute Classification for Cross-Domain Person Re-Identification

Simin Xu, Lingkun Luo, Shiqiang Hu

Responsive image

Auto-TLDR; An attention-based model with attribute classification for cross-domain person re-identification

Poster Similar

Person re-identification (re-ID) which aims to recognize a pedestrian observed by non-overlapping cameras is a challenging task due to high variance between images from different viewpoints. Although remarkable progresses on research of re-ID had been obtained via leveraging the merits of deep learning framework through sufficient quantity training on a large amount of well labeled data, whereas, in real scenarios, re-ID generally suffers from lacking of well labeled training data. In this paper, we propose an attention-based model with attribute classification (AMAC) to facilitate a well trained model transferring across different data domains, which further enables an efficient cross-domain video-based person re-ID. Specifically, an attention-based sub-network is proposed for deep insight into the quality variations of local parts, hence, different local parts are cooperated with different weights to avoid the heavy occlusions or the cluttered background in datasets. Moreover, we introduce a transferred attribute classification sub-network to extract attribute-semantic features of any new target datasets without the requirement for new training attribute labels which are costly to annotate. Attribute-semantic features can be considered as valuable complementary information for person re-identification since they are robust to illumination varieties and different viewpoints across cameras. Due to the large gap between different datasets, we finetune each sub-network with pseudo labels on the target datasets respectively to strengthen the original model trained on other labeled datasets. Extensive comparable evaluations demonstrate the superiority of our AMAC in solving cross-domain person re-ID task on two benchmarks including PRID-2011 and iLIDS-VID.

Robust Pedestrian Detection in Thermal Imagery Using Synthesized Images

My Kieu, Lorenzo Berlincioni, Leonardo Galteri, Marco Bertini, Andrew Bagdanov, Alberto Del Bimbo

Responsive image

Auto-TLDR; Improving Pedestrian Detection in the thermal domain using Generative Adversarial Network

Slides Poster Similar

In this paper we propose a method for improving pedestrian detection in the thermal domain using two stages: first, a generative data augmentation approach is used, then a domain adaptation method using generated data adapts an RGB pedestrian detector. Our model, based on the Least-Squares Generative Adversarial Network, is trained to synthesize realistic thermal versions of input RGB images which are then used to augment the limited amount of labeled thermal pedestrian images available for training. We apply our generative data augmentation strategy in order to adapt a pretrained YOLOv3 pedestrian detector to detection in the thermal-only domain. Experimental results demonstrate the effectiveness of our approach: using less than 50% of available real thermal training data, and relying on synthesized data generated by our model in the domain adaptation phase, our detector achieves state-of-the-art results on the KAIST Multispectral Pedestrian Detection Benchmark; even if more real thermal data is available adding GAN generated images to the training data results in improved performance, thus showing that these images act as an effective form of data augmentation. To the best of our knowledge, our detector achieves the best single-modality detection results on KAIST with respect to the state-of-the-art.

Multi-Level Deep Learning Vehicle Re-Identification Using Ranked-Based Loss Functions

Eleni Kamenou, Jesus Martinez-Del-Rincon, Paul Miller, Patricia Devlin - Hill

Responsive image

Auto-TLDR; Multi-Level Re-identification Network for Vehicle Re-Identification

Slides Poster Similar

Identifying vehicles across a network of cameras with non-overlapping fields of view remains a challenging research problem due to scene occlusions, significant inter-class similarity and intra-class variability. In this paper, we propose an end-to-end multi-level re-identification network that is capable of successfully projecting same identity vehicles closer to one another in the embedding space, compared to vehicles of different identities. Robust feature representations are obtained by combining features at multiple levels of the network. As for the learning process, we employ a recent state-of-the-art structured metric learning loss function previously applied to other retrieval problems and adjust it to the vehicle re-identification task. Furthermore, we explore the cases of image-to-image, image-to-video and video-to-video similarity metric. Finally, we evaluate our system and achieve great performance on two large-scale publicly available datasets, CityFlow-ReID and VeRi-776. Compared to most existing state-of-art approaches, our approach is simpler and more straightforward, utilizing only identity-level annotations, while avoiding post-processing the ranking results (re-ranking) at the testing phase.

Self-Paced Bottom-Up Clustering Network with Side Information for Person Re-Identification

Mingkun Li, Chun-Guang Li, Ruo-Pei Guo, Jun Guo

Responsive image

Auto-TLDR; Self-Paced Bottom-up Clustering Network with Side Information for Unsupervised Person Re-identification

Slides Poster Similar

Person re-identification (Re-ID) has attracted a lot of research attention in recent years. However, supervised methods demand an enormous amount of manually annotated data. In this paper, we propose a Self-Paced bottom-up Clustering Network with Side Information (SPCNet-SI) for unsupervised person Re-ID, where the side information comes from the serial number of the camera associated with each image. Specifically, our proposed SPCNet-SI exploits the camera side information to guide the feature learning and uses soft label in bottom-up clustering process, in which the camera association information is used in the repelled loss and the soft label based cluster information is used to select the candidate cluster pairs to merge. Moreover, a self-paced dynamic mechanism is developed to regularize the merging process such that the clustering is implemented in an easy-to-hard way with a slow-to-fast merging process. Experiments on two benchmark datasets Market-1501 and DukeMTMC-ReID demonstrate promising performance.

Recurrent Deep Attention Network for Person Re-Identification

Changhao Wang, Jun Zhou, Xianfei Duan, Guanwen Zhang, Wei Zhou

Responsive image

Auto-TLDR; Recurrent Deep Attention Network for Person Re-identification

Slides Poster Similar

Person re-identification (re-id) is an important task in video surveillance. It is challenging due to the appearance of person varying a wide range acrossnon-overlapping camera views. Recent years, attention-based models are introduced to learn discriminative representation. In this paper, we consider the attention selection in a natural way as like human moving attention on different parts of the visual field for person re-id. In concrete, we propose a Recurrent Deep Attention Network (RDAN) with an attention selection mechanism based on reinforcement learning. The RDAN aims to adaptively observe the identity-sensitive regions to build up the representation of individuals step by step. Extensive experiments on three person re-id benchmarks Market-1501, DukeMTMC-reID and CUHK03-NP demonstrate the proposed method can achieve competitive performance.

Spatial-Aware GAN for Unsupervised Person Re-Identification

Fangneng Zhan, Changgong Zhang

Responsive image

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

Similar

The recent person re-identification research has achieved great success by learning from a large number of labeled person images. On the other hand, the learned models often experience significant performance drops when applied to images collected in a different environment. Unsupervised domain adaptation (UDA) has been investigated to mitigate this constraint, but most existing systems adapt images at pixel level only and ignore obvious discrepancies at spatial level. This paper presents an innovative UDA-based person re-identification network that is capable of adapting images at both spatial and pixel levels simultaneously. A novel disentangled cycle-consistency loss is designed which guides the learning of spatial-level and pixel-level adaptation in a collaborative manner. In addition, a novel multi-modal mechanism is incorporated which is capable of generating images of different geometry views and augmenting training images effectively. Extensive experiments over a number of public datasets show that the proposed UDA network achieves superior person re-identification performance as compared with the state-of-the-art.

A Duplex Spatiotemporal Filtering Network for Video-Based Person Re-Identification

Chong Zheng, Ping Wei, Nanning Zheng

Responsive image

Auto-TLDR; Duplex Spatiotemporal Filtering Network for Person Re-identification in Videos

Slides Poster Similar

Video-based person re-identification plays important roles in surveillance video analysis. This paper proposes a novel Duplex Spatiotemporal Filtering Network (DSFN) to re-identify persons in videos. A video sequence is represented as a duplex spatiotemporal matrix. DSFN model containing a group of filters performs filtering at feature level in both temporal and spatial dimensions, by which the model focuses on feature-level semantic information rather than image-level information as in the traditional filters. We propose sparse-orthogonal constraints to enforce the model to extract more discriminative features. DSFN characterizes not only the appearance features but also dynamic information such as gaits embedded in video sequences and obtains a better performance as a result. Experiments show that the proposed method outperforms state-of-the-art approaches.

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

Mingliang Yang, Da Huang, Jing Zhao

Responsive image

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

Slides Poster Similar

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.

Semi-Supervised Person Re-Identification by Attribute Similarity Guidance

Peixian Hong, Ancong Wu, Wei-Shi Zheng

Responsive image

Auto-TLDR; Attribute Similarity Guidance Guidance Loss for Semi-supervised Person Re-identification

Slides Poster Similar

Although supervised person re-identification (RE-ID) has achieved great progress with deep learning, it requires time-consuming annotation of a large number of pedestrian identities. To reduce labeling cost, we attempt to reduce cross-camera identity annotations and exploit pedestrian attribute annotations as auxiliary information instead. The pedestrian attributes, such as outfit styles, contain coarse semantic knowledge. Although pedestrian attributes are annotated without exhaustive searching in a camera network, which is much easier than cross-camera identity annotation, ambiguity exists in attributes when different persons have similar outfits. To solve this problem, we propose an Attribute Similarity Guidance loss (ASG) to guide appearance feature learning for RE-ID by selective attribute similarity preservation to avoid the impact of such ambiguity. Finally, we develop an attribute-guided self training framework to jointly utilize attribute annotations, unlabeled data and limited labeled data for semi-supervised learning. Extensive experiments on Market-1501 and DukeMTMC-ReID show the superiority of our method for semi-supervised RE-ID.

How Important Are Faces for Person Re-Identification?

Julia Dietlmeier, Joseph Antony, Kevin Mcguinness, Noel E O'Connor

Responsive image

Auto-TLDR; Anonymization of Person Re-identification Datasets with Face Detection and Blurring

Slides Poster Similar

This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person re-identification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns.

Person Recognition with HGR Maximal Correlation on Multimodal Data

Yihua Liang, Fei Ma, Yang Li, Shao-Lun Huang

Responsive image

Auto-TLDR; A correlation-based multimodal person recognition framework that learns discriminative embeddings of persons by joint learning visual features and audio features

Slides Poster Similar

Multimodal person recognition is a common task in video analysis and public surveillance, where information from multiple modalities, such as images and audio extracted from videos, are used to jointly determine the identity of a person. Previous person recognition techniques either use only uni-modal data or only consider shared representations between different input modalities, while leaving the extraction of their relationship with identity information to downstream tasks. Furthermore, real-world data often contain noise, which makes recognition more challenging practical situations. In our work, we propose a novel correlation-based multimodal person recognition framework that is relatively simple but can efficaciously learn supervised information in multimodal data fusion and resist noise. Specifically, our framework learns a discriminative embeddings of persons by joint learning visual features and audio features while maximizing HGR maximal correlation among multimodal input and persons' identities. Experiments are done on a subset of Voxceleb2. Compared with state-of-the-art methods, the proposed method demonstrates an improvement of accuracy and robustness to noise.

Sample-Dependent Distance for 1 : N Identification Via Discriminative Feature Selection

Naoki Kawamura, Susumu Kubota

Responsive image

Auto-TLDR; Feature Selection Mask for 1:N Identification Problems with Binary Features

Slides Poster Similar

We focus on 1:N identification problems with binary features. Most multiclass classification methods, including identification and verification methods, use a shared metric space in which distances between samples are measured regardless of their identities. This is because dedicated metric spaces learned for each identity in the training set are of little use for the test set. In 1:N identification problems, however, gallery samples contain rich information about the test domain. Given a sample and its neighbors in the gallery set, we propose a method for calculating a discriminative feature selection mask that is used as a sample-dependent distance metric. Experiments on several re-identification datasets show that the proposed method enhances the performance of state-of-the-art feature extractors.

Decoupled Self-Attention Module for Person Re-Identification

Chao Zhao, Zhenyu Zhang, Jian Yang, Yan Yan

Responsive image

Auto-TLDR; Decoupled Self-attention Module for Person Re-identification

Slides Poster Similar

Person re-identification aims to identifying the same person from different cameras, which needs to integrate whole-body information and capture global correlation. However, convolutional neural network is able to only capture short-distance information because of the size of filters. Self-attention is introduced to capture long-distance correlation, but inner-product similarity calculation in self-attention mingles semantic response and semantic difference together. Semantic difference is more important for person re-identification, because it is robust to illumination without the effect of semantic response. However, we find the scale of norms measuring semantic response is much larger than angle measuring semantic difference by decoupling inner-product similarity into norms and angle. To balance the importance of semantic response and semantic difference in self-attention, we propose the decoupled self-attention module for person re-identification to make the most of self-attention. Extensive experiments show that the decoupled self-attention module obtains significant performance with easier convergence and stronger robustness.

Cross-Media Hash Retrieval Using Multi-head Attention Network

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

Responsive image

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

Slides Poster Similar

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.

SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Recognition

Wenshuang Liu, Wenting Chen, Yuanlue Zhu, Linlin Shen

Responsive image

Auto-TLDR; SATGAN: Stable Age Translation GAN for Cross-Age Face Recognition

Slides Poster Similar

In this paper, we propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR) . The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information. The quantitative and qualitative evaluations on Morph dataset prove that SATGAN achieves much better performance than existing methods. The face recognition model trained using dataset (VGGFace2 and MS-Celeb-1M) augmented using our SATGAN achieves better accuracy on cross age dataset like Cross-Age LFW and AgeDB-30.

SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition

Raphael Memmesheimer, Nick Theisen, Dietrich Paulus

Responsive image

Auto-TLDR; One-Shot Action Recognition using Metric Learning

Slides Similar

Recognizing an activity with a single reference sample using metric learning approaches is a promising research field. The majority of few-shot methods focus on object recognition or face-identification. We propose a metric learning approach to reduce the action recognition problem to a nearest neighbor search in embedding space. We encode signals into images and extract features using a deep residual CNN. Using triplet loss, we learn a feature embedding. The resulting encoder transforms features into an embedding space in which closer distances encode similar actions while higher distances encode different actions. Our approach is based on a signal level formulation and remains flexible across a variety of modalities. It further outperforms the baseline on the large scale NTU RGB+D 120 dataset for the One-Shot action recognition protocol by \ntuoneshotimpro%. With just 60% of the training data, our approach still outperforms the baseline approach by \ntuoneshotimproreduced%. With 40% of the training data, our approach performs comparably well as the second follow up. Further, we show that our approach generalizes well in experiments on the UTD-MHAD dataset for inertial, skeleton and fused data and the Simitate dataset for motion capturing data. Furthermore, our inter-joint and inter-sensor experiments suggest good capabilities on previously unseen setups.

Multi-Label Contrastive Focal Loss for Pedestrian Attribute Recognition

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

Responsive image

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

Slides Poster Similar

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.

Exemplar Guided Cross-Spectral Face Hallucination Via Mutual Information Disentanglement

Haoxue Wu, Huaibo Huang, Aijing Yu, Jie Cao, Zhen Lei, Ran He

Responsive image

Auto-TLDR; Exemplar Guided Cross-Spectral Face Hallucination with Structural Representation Learning

Slides Poster Similar

Recently, many Near infrared-visible (NIR-VIS) heterogeneous face recognition (HFR) methods have been proposed in the community. But it remains a challenging problem because of the sensing gap along with large pose variations. In this paper, we propose an Exemplar Guided Cross-Spectral Face Hallucination (EGCH) to reduce the domain discrepancy through disentangled representation learning. For each modality, EGCH contains a spectral encoder as well as a structure encoder to disentangle spectral and structure representation, respectively. It also contains a traditional generator that reconstructs the input from the above two representations, and a structure generator that predicts the facial parsing map from the structure representation. Besides, mutual information minimization and maximization are conducted to boost disentanglement and make representations adequately expressed. Then the translation is built on structure representations between two modalities. Provided with the transformed NIR structure representation and original VIS spectral representation, EGCH is capable to produce high-fidelity VIS images that preserve the topology structure of the input NIR while transfer the spectral information of an arbitrary VIS exemplar. Extensive experiments demonstrate that the proposed method achieves more promising results both qualitatively and quantitatively than the state-of-the-art NIR-VIS methods.

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

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

Responsive image

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

Slides Poster Similar

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.

Nighttime Pedestrian Detection Based on Feature Attention and Transformation

Gang Li, Shanshan Zhang, Jian Yang

Responsive image

Auto-TLDR; FAM and FTM: Enhanced Feature Attention Module and Feature Transformation Module for nighttime pedestrian detection

Slides Poster Similar

Pedestrian detection at nighttime is an important yet challenging task, which is fundamental for many practical applications, e.g. autonomous driving, video surveillance. To address this problem, in this work we start with some analysis, from which we find that the nighttime features have much more noise than that of daytime, resulting in low discrimination ability. Besides, we also observe some pedestrian examples are under adverse illumination conditions, and they can hardly provide sufficient information for accurate detection. Based on these findings, we propose the Feature Attention Module (FAM) and Feature Transformation Module (FTM) to enhance nighttime features. In FAM, guided by progressive segmentation supervision, hierarchical feature attention is produced to enhance multi-level features. On the other hand, FTM is introduced to enforce features from adverse illumination to approach that from better illumination. Based on feature attention and transformation (FAT) mechanism, a two-stage detector called FATNet is constructed for nighttime pedestrian detection. We conduct extensive experiments on nighttime datasets of EuroCity Persons (Night) and NightOwls to demonstrate the effectiveness of our method. On both two datasets, our method achieves significant improvements to the baseline and also outperforms state-of-the-art detectors.

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

Li Ren, Kai Li, Liqiang Wang, Kien Hua

Responsive image

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

Slides Poster Similar

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.

Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification

Tetsu Matsukawa, Einoshin Suzuki

Responsive image

Auto-TLDR; A small-scale CNN feature transfer method for person re-identification

Slides Poster Similar

Modern Convolutional Neural Networks~(CNNs) have been improving the accuracy of person re-identification (re-id) using a large number of training samples. Such a re-id system suffers from a lack of training samples for deployment to practical security applications. To address this problem, we focus on the approach that transfers CNN features pre-trained on a large-scale person re-id dataset to a small-scale dataset. Most of the ordinal CNN feature transfer methods use the features of fully connected layers that entangle locally pooled features of different spatial locations on an image. Unfortunately, due to the difference of view angles and the bias of walking directions of the persons, each camera view in a dataset has a unique spatial property in the person image, which reduces the generality of the local pooling for different cameras/datasets. To account for the camera- and dataset-specific spatial bias, we propose a method to learn camera and dataset-specific position weight maps for discriminative local pooling of convolutional features. Our experiments on four public datasets confirm the effectiveness of the proposed feature transfer with a small number of training samples in the target datasets.

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

Jingzhi Li, Lutong Han, Hua Zhang, Xiaoguang Han, Jingguo Ge, Xiaochu Cao

Responsive image

Auto-TLDR; Individual Face Privacy under Surveillance Scenario with Multi-task Loss Function

Poster Similar

In this paper, we focus on protecting the person face privacy under the surveillance scenarios, whose goal is to change the visual appearances of faces while keep them to be recognizable by current face recognition systems. This is a challenging problem as that we should retain the most important structures of captured facial images, while alter the salient facial regions to protect personal privacy. To address this problem, we introduce a novel individual face protection model, which can camouflage the face appearance from the perspective of human visual perception and preserve the identity features of faces used for face authentication. To that end, we develop an encoder-decoder network architecture that can separately disentangle the person feature representation into an appearance code and an identity code. Specifically, we first randomly divide the face image into two groups, the source set and the target set, where the source set is used to extract the identity code and the target set provides the appearance code. Then, we recombine the identity and appearance codes to synthesize a new face, which has the same identity with the source subject. Finally, the synthesized faces are used to replace the original face to protect the privacy of individual. Furthermore, our model is trained end-to-end with a multi-task loss function, which can better preserve the identity and stabilize the training loss. Experiments conducted on Cross-Age Celebrity dataset demonstrate the effectiveness of our model and validate our superiority in terms of visual quality and scalability.

DAIL: Dataset-Aware and Invariant Learning for Face Recognition

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

Responsive image

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

Slides Poster Similar

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.

Unsupervised Disentangling of Viewpoint and Residues Variations by Substituting Representations for Robust Face Recognition

Minsu Kim, Joanna Hong, Junho Kim, Hong Joo Lee, Yong Man Ro

Responsive image

Auto-TLDR; Unsupervised Disentangling of Identity, viewpoint, and Residue Representations for Robust Face Recognition

Slides Poster Similar

It is well-known that identity-unrelated variations (e.g., viewpoint or illumination) degrade the performances of face recognition methods. In order to handle this challenge, a robust method for disentangling the identity and view representations has drawn an attention in the machine learning area. However, existing methods learn discriminative features which require a manual supervision of such factors of variations. In this paper, we propose a novel disentangling framework through modeling three representations of identity, viewpoint, and residues (i.e., identity and pose unrelated) which do not require supervision of the variations. By jointly modeling the three representations, we enhance the disentanglement of each representation and achieve robust face recognition performance. Further, the learned viewpoint representation can be utilized for pose estimation or editing of a posed facial image. Extensive quantitative and qualitative evaluations verify the effectiveness of our proposed method which disentangles identity, viewpoint, and residues of facial images.

One-Shot Representational Learning for Joint Biometric and Device Authentication

Sudipta Banerjee, Arun Ross

Responsive image

Auto-TLDR; Joint Biometric and Device Recognition from a Single Biometric Image

Slides Poster Similar

In this work, we propose a method to simultaneously perform (i) biometric recognition (\textit{i.e.}, identify the individual), and (ii) device recognition, (\textit{i.e.}, identify the device) from a single biometric image, say, a face image, using a one-shot schema. Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy. We propose to automatically learn a joint representation that encapsulates both biometric-specific and sensor-specific features. We evaluate the proposed approach using iris, face and periocular images acquired using near-infrared iris sensors and smartphone cameras. Experiments conducted using 14,451 images from 13 sensors resulted in a rank-1 identification accuracy of upto 99.81\% and a verification accuracy of upto 100\% at a false match rate of 1\%.

Thermal Image Enhancement Using Generative Adversarial Network for Pedestrian Detection

Mohamed Amine Marnissi, Hajer Fradi, Anis Sahbani, Najoua Essoukri Ben Amara

Responsive image

Auto-TLDR; Improving Visual Quality of Infrared Images for Pedestrian Detection Using Generative Adversarial Network

Slides Poster Similar

Infrared imaging has recently played an important role in a wide range of applications including surveillance, robotics and night vision. However, infrared cameras often suffer from some limitations, essentially about low-contrast and blurred details. These problems contribute to the loss of observation of target objects in infrared images, which could limit the feasibility of different infrared imaging applications. In this paper, we mainly focus on the problem of pedestrian detection on thermal images. Particularly, we emphasis the need for enhancing the visual quality of images beforehand performing the detection step. % to ensure effective results. To address that, we propose a novel thermal enhancement architecture based on Generative Adversarial Network, and composed of two modules contrast enhancement and denoising modules with a post-processing step for edge restoration in order to improve the overall quality. The effectiveness of the proposed architecture is assessed by means of visual quality metrics and better results are obtained compared to the original thermal images and to the obtained results by other existing enhancement methods. These results have been conduced on a subset of KAIST dataset. Using the same dataset, the impact of the proposed enhancement architecture has been demonstrated on the detection results by obtaining better performance with a significant margin using YOLOv3 detector.

Loop-closure detection by LiDAR scan re-identification

Jukka Peltomäki, Xingyang Ni, Jussi Puura, Joni-Kristian Kamarainen, Heikki Juhani Huttunen

Responsive image

Auto-TLDR; Loop-Closing Detection from LiDAR Scans Using Convolutional Neural Networks

Slides Poster Similar

In this work, loop-closure detection from LiDAR scans is defined as an image re-identification problem. Re-identification is performed by computing Euclidean distances of a query scan to a gallery set of previous scans. The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN). The network is trained using the triplet loss training strategy. In our experiments we compare different backbone networks, variants of the triplet loss and generic and LiDAR specific data augmentation techniques. With a realistic indoor dataset the best architecture obtains the mean average precision (mAP) above 90%.

Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation

Bin Duan, Wei Wang, Hao Tang, Hugo Latapie, Yan Yan

Responsive image

Auto-TLDR; Cascade Attention-Guided Residue GAN for Cross-modal Audio-Visual Learning

Slides Poster Similar

Since we were babies, we intuitively develop the ability to correlate the input from different cognitive sensors such as vision, audio, and text. However, in machine learning, this cross-modal learning is a nontrivial task because different modalities have no homogeneous properties. Previous works discover that there should be bridges among different modalities. From neurology and psychology perspective, humans have the capacity to link one modality with another one, e.g., associating a picture of a bird with the only hearing of its singing and vice versa. Is it possible for machine learning algorithms to recover the scene given the audio signal? In this paper, we propose a novel Cascade Attention-Guided Residue GAN (CAR-GAN), aiming at reconstructing the scenes given the corresponding audio signals. Particularly, we present a residue module to mitigate the gap between different modalities progressively. Moreover, a cascade attention guided network with a novel classification loss function is designed to tackle the cross-modal learning task. Our model keeps consistency in the high-level semantic label domain and is able to balance two different modalities. The experimental results demonstrate that our model achieves the state-of-the-art cross-modal audio-visual generation on the challenging Sub-URMP dataset.

Domain Siamese CNNs for Sparse Multispectral Disparity Estimation

David-Alexandre Beaupre, Guillaume-Alexandre Bilodeau

Responsive image

Auto-TLDR; Multispectral Disparity Estimation between Thermal and Visible Images using Deep Neural Networks

Slides Poster Similar

Multispectral disparity estimation is a difficult task for many reasons: it as all the same challenges as traditional visible-visible disparity estimation (occlusions, repetitive patterns, textureless surfaces), in addition of having very few common visual information between images (e.g. color information vs. thermal information). In this paper, we propose a new CNN architecture able to do disparity estimation between images from different spectrum, namely thermal and visible in our case. Our proposed model takes two patches as input and proceeds to do domain feature extraction for each of them. Features from both domains are then merged with two fusion operations, namely correlation and concatenation. These merged vectors are then forwarded to their respective classification heads, which are responsible for classifying the inputs as being same or not. Using two merging operations gives more robustness to our feature extraction process, which leads to more precise disparity estimation. Our method was tested using the publicly available LITIV 2014 and LITIV 2018 datasets, and showed best results when compared to other state of the art methods.

Boundary Guided Image Translation for Pose Estimation from Ultra-Low Resolution Thermal Sensor

Kohei Kurihara, Tianren Wang, Teng Zhang, Brian Carrington Lovell

Responsive image

Auto-TLDR; Pose Estimation on Low-Resolution Thermal Images Using Image-to-Image Translation Architecture

Slides Poster Similar

This work addresses the pose estimation task on low-resolution images captured using thermal sensors which can operate in a no-light environment. Low-resolution thermal sensors have been widely adopted in various applications for cost control and privacy protection purposes. In this paper, targeting the challenging scenario of ultra-low resolution thermal imaging (3232 pixels), we aim to estimate human poses for the purpose of monitoring health conditions and indoor events. To overcome the challenges in ultra-low resolution thermal imaging such as blurred boundaries and data scarcity, we propose a new Image-to-Image (I2I) translation architecture which can translate the original blurred thermal image into a visible light image with sharper boundaries. Then the generated visible light image can be fed into the off-the-shelf pose estimator which was well-trained in the visible domain. Experimental results suggest that the proposed framework outperforms other state-of-the-art methods in the I2I based pose estimation task for our thermal image dataset. Furthermore, we also demonstrated the merits of the proposed method on the publicly available FLIR dataset by measuring the quality of translated images.