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Spatial-Aware GAN for Unsupervised Person Re-Identification

Fangneng Zhan, Changgong Zhang

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

Abstract

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.

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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

Abstract 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.

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

Abstract 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.

Pose Variation Adaptation for Person Re-Identification

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

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

Abstract 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.

Augmented Cyclic Consistency Regularization for Unpaired Image-To-Image Translation

Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, Nakamasa Inoue

Auto-TLDR; Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation

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Unpaired image-to-image (I2I) translation has received considerable attention in pattern recognition and computer vision because of recent advancements in generative adversarial networks (GANs). However, due to the lack of explicit supervision, unpaired I2I models often fail to generate realistic images, especially in challenging datasets with different backgrounds and poses. Hence, stabilization is indispensable for real-world applications and GANs. Herein, we propose Augmented Cyclic Consistency Regularization (ACCR), a novel regularization method for unpaired I2I translation. Our main idea is to enforce consistency regularization originating from semi-supervised learning on the discriminators leveraging real, fake, reconstructed, and augmented samples. We regularize the discriminators to output similar predictions when fed pairs of original and perturbed images. We qualitatively clarify the generation property between unpaired I2I models and standard GANs, and explain why consistency regularization on fake and reconstructed samples works well. Quantitatively, our method outperforms the consistency regularized GAN (CR-GAN) in real-world translations and demonstrates efficacy against several data augmentation variants and cycle-consistent constraints.

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

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

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

Abstract 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/.

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

Abstract Slides Poster Similar

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.

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

Abstract Slides Poster Similar

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.

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

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

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

Abstract 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.

Unsupervised Multi-Task Domain Adaptation

Shih-Min Yang, Mei-Chen Yeh

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

Abstract Slides Poster Similar

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.

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

Vladislav Sovrasov, Dmitry Sidnev

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

Abstract 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.

RGB-Infrared Person Re-Identification Via Image Modality Conversion

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

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

Abstract 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.

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

Abstract Slides Poster Similar

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.

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

Abstract Slides Poster Similar

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

Abstract 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.

Online Domain Adaptation for Person Re-Identification with a Human in the Loop

Rita Delussu, Lorenzo Putzu, Giorgio Fumera, Fabio Roli

Auto-TLDR; Human-in-the-loop for Person Re-Identification in Infeasible Applications

Abstract Slides Poster Similar

Supervised deep learning methods have recently achieved remarkable performance in person re-identification. Unsupervised domain adaptation (UDA) approaches have also been proposed for application scenarios where only unlabelled data are available from target camera views. We consider a more challenging scenario when even collecting a suitable amount of representative, unlabelled target data for offline training or fine-tuning is infeasible. In this context we revisit the human-in-the-loop (HITL) approach, which exploits online the operator's feedback on a small amount of target data. We argue that HITL is a kind of online domain adaptation specifically suited to person re-identification. We then reconsider relevance feedback methods for content-based image retrieval that are computationally much cheaper than state-of-the-art HITL methods for person re-identification, and devise a specific feedback protocol for them. Experimental results show that HITL can achieve comparable or better performance than UDA, and is therefore a valid alternative when the lack of unlabelled target data makes UDA infeasible.

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

Simin Xu, Lingkun Luo, Shiqiang Hu

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

Abstract 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.

Self and Channel Attention Network for Person Re-Identification

Asad Munir, Niki Martinel, Christian Micheloni

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

Abstract 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.

Detail Fusion GAN: High-Quality Translation for Unpaired Images with GAN-Based Data Augmentation

Ling Li, Yaochen Li, Chuan Wu, Hang Dong, Peilin Jiang, Fei Wang

Auto-TLDR; Data Augmentation with GAN-based Generative Adversarial Network

Abstract Slides Poster Similar

Image-to-image translation, a task to learn the mapping relation between two different domains, is a rapid-growing research field in deep learning. Although existing Generative Adversarial Network(GAN)-based methods have achieved decent results in this field, there are still some limitations in generating high-quality images for practical applications (e.g., data augmentation and image inpainting). In this work, we aim to propose a GAN-based network for data augmentation which can generate translated images with more details and less artifacts. The proposed Detail Fusion Generative Adversarial Network(DFGAN) consists of a detail branch, a transfer branch, a filter module, and a reconstruction module. The detail branch is trained by a super-resolution loss and its intermediate features can be used to introduce more details to the transfer branch by the filter module. Extensive evaluations demonstrate that our model generates more satisfactory images against the state-of-the-art approaches for data augmentation.

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

Abstract Slides Poster Similar

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.

Semi-Supervised Person Re-Identification by Attribute Similarity Guidance

Peixian Hong, Ancong Wu, Wei-Shi Zheng

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

Abstract 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

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

Abstract 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.

Class Conditional Alignment for Partial Domain Adaptation

Mohsen Kheirandishfard, Fariba Zohrizadeh, Farhad Kamangar

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

Abstract Slides Poster Similar

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.

Galaxy Image Translation with Semi-Supervised Noise-Reconstructed Generative Adversarial Networks

Qiufan Lin, Dominique Fouchez, Jérôme Pasquet

Auto-TLDR; Semi-supervised Image Translation with Generative Adversarial Networks Using Paired and Unpaired Images

Abstract Slides Poster Similar

Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images. However, current work is limited to utilizing paired images with supervised translation, and there has been rare discussion on reconstructing noise background that encodes instrumental and observational effects. These limitations might be harmful for subsequent scientific applications in astrophysics. Therefore, we aim to develop methods for using unpaired images and preserving noise characteristics in image translation. In this work, we propose a two-way image translation model using GANs that exploits both paired and unpaired images in a semi-supervised manner, and introduce a noise emulating module that is able to learn and reconstruct noise characterized by high-frequency features. By experimenting on multi-band galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France Hawaii Telescope Legacy Survey (CFHT), we show that our method recovers global and local properties effectively and outperforms benchmark image translation models. To our best knowledge, this work is the first attempt to apply semi-supervised methods and noise reconstruction techniques in astrophysical studies.

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

Abstract Slides Poster Similar

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.

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.

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

Tetsu Matsukawa, Einoshin Suzuki

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

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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 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.

Attentive Part-Aware Networks for Partial Person Re-Identification

Lijuan Huo, Chunfeng Song, Zhengyi Liu, Zhaoxiang Zhang

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

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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.

Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation

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

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

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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.

Cycle-Consistent Adversarial Networks and Fast Adaptive Bi-Dimensional Empirical Mode Decomposition for Style Transfer

Elissavet Batziou, Petros Alvanitopoulos, Konstantinos Ioannidis, Ioannis Patras, Stefanos Vrochidis, Ioannis Kompatsiaris

Auto-TLDR; FABEMD: Fast and Adaptive Bidimensional Empirical Mode Decomposition for Style Transfer on Images

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Recently, research endeavors have shown the potentiality of Cycle-Consistent Adversarial Networks (CycleGAN) in style transfer. In Cycle-Consistent Adversarial Networks, the consistency loss is introduced to measure the difference between the original images and the reconstructed in both directions, forward and backward. In this work, the combination of Cycle-Consistent Adversarial Networks with Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is proposed to perform style transfer on images. In the proposed approach the cycle-consistency loss is modified to include the differences between the extracted Intrinsic Mode Functions (BIMFs) images. Instead of an estimation of pixel-to-pixel difference between the produced and input images, the FABEMD is applied and the extracted BIMFs are involved in the computation of the total cycle loss. This method enriches the computation of the total loss in a content-to-content and style-to-style comparison by connecting the spatial information to the frequency components. The experimental results reveal that the proposed method is efficient and produces qualitative results comparable to state-of-the-art methods.

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.

Attentional Wavelet Network for Traditional Chinese Painting Transfer

Rui Wang, Huaibo Huang, Aihua Zheng, Ran He

Auto-TLDR; Attentional Wavelet Network for Photo to Chinese Painting Transfer

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Traditional Chinese paintings pay more attention to ’Gongbi’ and ’Xieyi’ in artworks, which raises a challenging task to generate Chinese paintings from photos. ’Xieyi’ creates high-level conception for paintings, while ’Gongbi’ refers to portraying local details in paintings. This paper proposes an attentional wavelet network for photo to Chinese painting transferring. We first introduce wavelets to obtain high-level conception and local details in Chinese paintings via 2-D haar wavelet transform. Moreover, we design high-level transform stream and local enhancement stream to dispose high frequencies and low frequency respectively. Furthermore, we exploit self-attention mechanism to compatibly pick up high-level information which is used to remedy the missing details when reconstructing the Chinese painting. To advance our experiment, we set up a new dataset named P2ADataset, with diverse photos and Chinese paintings on famous mountains around China. Experimental results comparing with the state-of-the-art style transferring algorithms verify the effectiveness of the proposed method. We will release the codes and data to the public.

Local Facial Attribute Transfer through Inpainting

Ricard Durall, Franz-Josef Pfreundt, Janis Keuper

Auto-TLDR; Attribute Transfer Inpainting Generative Adversarial Network

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The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our Attribute Transfer Inpainting Generative Adversarial Network (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.

Recurrent Deep Attention Network for Person Re-Identification

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

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

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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.

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.

Adaptive L2 Regularization in Person Re-Identification

Xingyang Ni, Liang Fang, Heikki Juhani Huttunen

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

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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.

Decoupled Self-Attention Module for Person Re-Identification

Chao Zhao, Zhenyu Zhang, Jian Yang, Yan Yan

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

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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.

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

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

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

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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.

Multi-Domain Image-To-Image Translation with Adaptive Inference Graph

The Phuc Nguyen, Stéphane Lathuiliere, Elisa Ricci

Auto-TLDR; Adaptive Graph Structure for Multi-Domain Image-to-Image Translation

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In this work, we address the problem of multi-domain image-to-image translation with particular attention paid to computational cost. In particular, current state of the art models require a large and deep model in order to handle the visual diversity of multiple domains. In a context of limited computational resources, increasing the network size may not be possible. Therefore, we propose to increase the network capacity by using an adaptive graph structure. At inference time, the network estimates its own graph by selecting specific sub-networks. Sub-network selection is implemented using Gumble-Softmax in order to allow end-to-end training. This approach leads to an adjustable increase in number of parameters while preserving an almost constant computational cost. Our evaluation on two publicly available datasets of facial and painting images shows that our adaptive strategy generates better images with fewer artifacts than literature methods.

Deep Top-Rank Counter Metric for Person Re-Identification

Chen Chen, Hao Dou, Xiyuan Hu, Silong Peng

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

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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.

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.

Efficient Shadow Detection and Removal Using Synthetic Data with Domain Adaptation

Rui Guo, Babajide Ayinde, Hao Sun

Auto-TLDR; Shadow Detection and Removal with Domain Adaptation and Synthetic Image Database

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In recent years, learning based shadow detection and removal approaches have shown prospects and, in most cases, yielded state-of-the-art results. The performance of these approaches, however, relies heavily on the construction of training database of shadow images, shadow-free versions, and shadow maps as ground truth. This conventional data gathering method is time-consuming, expensive, or even practically intractable to realize especially for outdoor scenes with complicated shadow patterns, thus limiting the size of the data available for training. In this paper, we leverage on large high quality synthetic image database and domain adaptation to eliminate the bottlenecks resulting from insufficient training samples and domain bias. Specifically, our approach utilizes adversarial training to predict near-pixel-perfect shadow map from synthetic shadow image for downstream shadow removal steps. At inference time, we capitalize on domain adaptation via image style transfer to map the style of real- world scene to that of synthetic scene for the purpose of detecting and subsequently removing shadow. Comprehensive experiments indicate that our approach outperforms state-of-the-art methods on select benchmark datasets.

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.

Robust Pedestrian Detection in Thermal Imagery Using Synthesized Images

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

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

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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.

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

Hong Liu, Ziling Miao, Bing Yang, Runwei Ding

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

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

Open-World Group Retrieval with Ambiguity Removal: A Benchmark

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

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

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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.

Unsupervised Domain Adaptation for Object Detection in Cultural Sites

Giovanni Pasqualino, Antonino Furnari, Giovanni Maria Farinella

Auto-TLDR; Unsupervised Domain Adaptation for Object Detection in Cultural Sites

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The ability to detect objects in cultural sites from the egocentric point of view of the user can enable interesting applications for both the visitors and the manager of the site. Unfortunately, current object detection algorithms have to be trained on large amounts of labeled data, the collection of which is costly and time-consuming. While synthetic data generated from the 3D model of the cultural site can be used to train object detection algorithms, a significant drop in performance is generally observed when such algorithms are deployed to work with real images. In this paper, we consider the problem of unsupervised domain adaptation for object detection in cultural sites. Specifically, we assume the availability of synthetic labeled images and real unlabeled images for training. To study the problem, we propose a dataset containing 75244 synthetic and 2190 real images with annotations for 16 different artworks. We hence investigate different domain adaptation techniques based on image-to-image translation and feature alignment. Our analysis points out that such techniques can be useful to address the domain adaptation issue, while there is still plenty of space for improvement on the proposed dataset. We release the dataset at our web page to encourage research on this challenging topic: https://iplab.dmi.unict.it/EGO-CH-OBJ-ADAPT/.

Identity-Preserved Face Beauty Transformation with Conditional Generative Adversarial Networks

Zhitong Huang, Ching Y Suen

Auto-TLDR; Identity-preserved face beauty transformation using conditional GANs

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Identity-preserved face beauty transformation aims to change the beauty scale of a face image while preserving the identity of the original face. In our framework of conditional Generative Adversarial Networks (cGANs), the synthesized face produced by the generator would have the same beauty scale indicated by the input condition. Unlike the discrete class labels used in most cGANs, the condition of target beauty scale in our framework is given by a continuous real-valued beauty score in the range [1 to 5], which makes the work challenging. To tackle the problem, we have implemented a triple structure, in which the conditional discriminator is divided into a normal discriminator and a separate face beauty predictor. We have also developed another new structure called Conditioned Instance Normalization to replace the original concatenation used in cGANs, which makes the combination of the input image and condition more effective. Furthermore, Self-Consistency Loss is introduced as a new parameter to improve the stability of training and quality of the generated image. In the end, the objectives of beauty transformation and identity preservation are evaluated by the pretrained face beauty predictor and state-of-the-art face recognition network. The result is encouraging and it also shows that certain facial features could be synthesized by the generator according to the target beauty scale, while preserving the original identity.