Deep Gait Relative Attribute Using a Signed Quadratic Contrastive Loss

Yuta Hayashi, Shehata Allam, Yasushi Makihara, Daigo Muramatsu, Yasushi Yagi

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Auto-TLDR; Signal-Contrastive Loss for Gait Attributes Estimation

This paper presents a deep learning-based method to estimate gait attributes (e.g., stately, cool, relax, etc.). Similarly to the existing studies on relative attribute, human perception-based annotations on the gait attributes are given to pairs of gait videos (i.e., the first one is better, tie, and the second one is better), and the relative annotations are utilized to train a ranking model of the gait attribute. More specifically, we design a Siamese (i.e., two-stream) network which takes a pair of gait inputs and output gait attribute score for each. We then introduce a suitable loss function called a signed contrastive loss to train the network parameters with the relative annotation. Unlike the existing loss functions for learning to rank does not inherent a nice property of a quadratic contrastive loss, the proposed signed quadratic contrastive loss function inherents the nice property. The quantitative evaluation results reveal that the proposed method shows better or comparable accuracies of relative attribute prediction against the baseline methods.

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Alireza Sepas-Moghaddam, Saeed Ghorbani, Nikolaus F. Troje, Ali Etemad

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Auto-TLDR; Learning to Transfer Multi-scale Partial Gait Representations using Capsule Networks for Gait Recognition

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Part-Based Collaborative Spatio-Temporal Feature Learning for Cloth-Changing Gait Recognition

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Auto-TLDR; Part-based Spatio-Temporal Feature Learning for Gait Recognition

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

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

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

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

Multi-Label Contrastive Focal Loss for Pedestrian Attribute Recognition

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

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Learning Natural Thresholds for Image Ranking

Somayeh Keshavarz, Quang Nhat Tran, Richard Souvenir

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Auto-TLDR; Image Representation Learning and Label Discretization for Natural Image Ranking

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Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Nikolaos Bakalos

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Auto-TLDR; Tensor-Based Neural Network for Spatiotemporal Pose Classifiaction using Three-Dimensional Skeleton Data

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Deep Top-Rank Counter Metric for Person Re-Identification

Chen Chen, Hao Dou, Xiyuan Hu, Silong Peng

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

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Video Analytics Gait Trend Measurement for Fall Prevention and Health Monitoring

Lawrence O'Gorman, Xinyi Liu, Md Imran Sarker, Mariofanna Milanova

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Auto-TLDR; Towards Health Monitoring of Gait with Deep Learning

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

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Gender Classification Using Video Sequences of Body Sway Recorded by Overhead Camera

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Auto-TLDR; Spatio-Temporal Feature for Gender Classification of a Standing Person Using Body Stance Using Time-Series Signals

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

Raphael Memmesheimer, Nick Theisen, Dietrich Paulus

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

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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.

A Grid-Based Representation for Human Action Recognition

Soufiane Lamghari, Guillaume-Alexandre Bilodeau, Nicolas Saunier

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Auto-TLDR; GRAR: Grid-based Representation for Action Recognition in Videos

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Learning Semantic Representations Via Joint 3D Face Reconstruction and Facial Attribute Estimation

Zichun Weng, Youjun Xiang, Xianfeng Li, Juntao Liang, Wanliang Huo, Yuli Fu

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Auto-TLDR; Joint Framework for 3D Face Reconstruction with Facial Attribute Estimation

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An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks

Debapriya Roy, Diganta Mukherjee, Bhabatosh Chanda

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Auto-TLDR; Unsupervised Skin Tone Change Using Augmented Reality Based Models

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Siamese-Structure Deep Neural Network Recognizing Changes in Facial Expression According to the Degree of Smiling

Kazuaki Kondo, Taichi Nakamura, Yuichi Nakamura, Shin'Ichi Satoh

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Auto-TLDR; A Siamese-Structure Deep Neural Network for Happiness Recognition

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A smile is a representative expression of happiness or high quality-of-life; however, automatic recognition of a smile according to happiness remains a challenging task. Because expressions of happiness are strongly dependent upon physical condition and occurrence of other emotions, and similar facial expression often occur under different emotions, we consider that there is no absolute visual pattern of a smile corresponding to happiness. Therefore, in this study, we assumed that a ``smile with happiness'' is observed as the temporal ascent in the degree of smiling and attempted to recognize this by capturing changes in facial expression within temporally sequential images. As an implementation of this scheme, we proposed a Siamese-structure deep neural network to compare facial expressions in two input images and estimate the existence of smile ascension or descension. For primal analysis of the proposed network, we developed a unique smiling dataset containing image pairs with various changes in smiling degree, including slight changes. The results demonstrated that the proposed method achieved nearly perfect recognition with >0.95 accuracy when recognizing changes in the degree of smiling that humans certainly recognize. Attention regions that contributed to the predicted labels were concentrated on the mouth, cheeks, and tail of the eyes, which indicates a reasonable function for recognizing changes in smiling degree was constructed by the proposed method.

How Unique Is a Face: An Investigative Study

Michal Balazia, S L Happy, Francois Bremond, Antitza Dantcheva

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Auto-TLDR; Uniqueness of Face Recognition: Exploring the Impact of Factors such as image resolution, feature representation, database size, age and gender

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Face recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector. Surprisingly, while widely accepted, we still lack the understanding of the uniqueness or distinctiveness of face as a biometric characteristic. In this work, we study the impact of factors such as image resolution, feature representation, database size, age and gender on uniqueness denoted by the Kullback-Leibler divergence between genuine and impostor distributions. Towards understanding the impact, we present experimental results on the datasets AT&T, LFW, IMDb-Face, as well as ND-TWINS, with the feature extraction algorithms VGGFace, VGG16, ResNet50, InceptionV3, MobileNet and DenseNet121, that reveal the quantitative impact of the named factors. While these are early results, our findings indicate the need for a better understanding of the concept of biometric uniqueness and its implication on face recognition.

Weight Estimation from an RGB-D Camera in Top-View Configuration

Marco Mameli, Marina Paolanti, Nicola Conci, Filippo Tessaro, Emanuele Frontoni, Primo Zingaretti

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Auto-TLDR; Top-View Weight Estimation using Deep Neural Networks

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The development of so-called soft-biometrics aims at providing information related to the physical and behavioural characteristics of a person. This paper focuses on bodyweight estimation based on the observation from a top-view RGB-D camera. In fact, the capability to estimate the weight of a person can be of help in many different applications, from health-related scenarios to business intelligence and retail analytics. To deal with this issue, a TVWE (Top-View Weight Estimation) framework is proposed with the aim of predicting the weight. The approach relies on the adoption of Deep Neural Networks (DNNs) that have been trained on depth data. Each network has also been modified in its top section to replace classification with prediction inference. The performance of five state-of-art DNNs has been compared, namely VGG16, ResNet, Inception, DenseNet and Efficient-Net. In addition, a convolutional auto-encoder has also been included for completeness. Considering the limited literature in this domain, the TVWE framework has been evaluated on a new publicly available dataset: “VRAI Weight estimation Dataset”, which also collects, for each subject, labels related to weight, gender, and height. The experimental results have demonstrated that the proposed methods are suitable for this task, bringing different and significant insights for the application of the solution in different domains.

Attribute-Based Quality Assessment for Demographic Estimation in Face Videos

Fabiola Becerra-Riera, Annette Morales-González, Heydi Mendez-Vazquez, Jean-Luc Dugelay

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Auto-TLDR; Facial Demographic Estimation in Video Scenarios Using Quality Assessment

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Most existing works regarding facial demographic estimation are focused on still image datasets, although nowadays the need to analyze video content in real applications is increasing. We propose to tackle gender, age and ethnicity estimation in the context of video scenarios. Our main contribution is to use an attribute-specific quality assessment procedure to select best quality frames from a video sequence for each of the three demographic modalities. Best quality frames are classified with fine-tuned MobileNet models and a final video prediction is obtained with a majority voting strategy among the best selected frames. Our validation on three different datasets and our comparison with state-of-the-art models, show the effectiveness of the proposed demographic classifiers and the quality pipeline, which allows to reduce both: the number of frames to be classified and the processing time in practical applications; and improves the soft biometrics prediction accuracy.

Open-World Group Retrieval with Ambiguity Removal: A Benchmark

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

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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.

3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties

Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Imperial London, Sergiy Bokhnyak, Susan Walsh, Mark Shriver, Michael Bronstein, Peter Claes

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Auto-TLDR; Multi-biometric Fusion for Biometric Verification using 3D Facial Mesures

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Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pipeline is presented for matching 3D facial shape to multiple DNA-related properties (sex, age, BMI and genomic background). The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. Most studies in the field of metric learning have only focused on Euclidean data. In this work, geometric deep learning is employed to learn directly from 3D facial meshes. To this end, spiral convolutions are used along with a novel mesh-sampling scheme that retains uniformly sampled 3D points at different levels of resolution. The second step is a multi-biometric fusion by a fully connected neural network. The network takes an ensemble of embeddings and property labels as input and returns genuine and imposter scores. Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently. Results obtained by a 10-fold cross-validation for biometric verification show that combining multiple properties leads to stronger biometric systems. Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve Bayes-based score-fuser.

What and How? Jointly Forecasting Human Action and Pose

Yanjun Zhu, Yanxia Zhang, Qiong Liu, Andreas Girgensohn

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Auto-TLDR; Forecasting Human Actions and Motion Trajectories with Joint Action Classification and Pose Regression

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Forecasting human actions and motion trajectories addresses the problem of predicting what a person is going to do next and how they will perform it. This is crucial in a wide range of applications such as assisted living and future co-robotic settings. We propose to simultaneously learn actions and action-related human motion dynamics, while existing works perform them independently. In this paper, we present a method to jointly forecast categories of human action and the pose of skeletal joints in the hope that the two tasks can help each other. As a result, our system can predict not only the future actions but also the motion trajectories that will result. To achieve this, we define a task of joint action classification and pose regression. We employ a sequence to sequence encoder-decoder model combined with multi-task learning to forecast future actions and poses progressively before the action happens. Experimental results on two public datasets, IkeaDB and OAD, demonstrate the effectiveness of the proposed method.

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

Mingjing Ai, Guozhi Shan, Bo Liu, Tianyang Liu

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

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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.

LFIR2Pose: Pose Estimation from an Extremely Low-Resolution FIR Image Sequence

Saki Iwata, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa

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Auto-TLDR; LFIR2Pose: Human Pose Estimation from a Low-Resolution Far-InfraRed Image Sequence

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In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array. Human body estimation from such a single LFIR image is a hard task. For training the estimation model, annotation of the human pose to the images is also a difficult task for human. Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training. Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference. Through an experiment, we evaluated the human pose estimation accuracy using an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.

Rank-Based Ordinal Classification

Joan Serrat, Idoia Ruiz

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Auto-TLDR; Ordinal Classification with Order

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Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class {\em labels}. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1\ldots $C$, being $C$ the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to less likely. This is tanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, \textit{i.e.}, most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset.

Vision-Based Multi-Modal Framework for Action Recognition

Djamila Romaissa Beddiar, Mourad Oussalah, Brahim Nini

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Auto-TLDR; Multi-modal Framework for Human Activity Recognition Using RGB, Depth and Skeleton Data

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Human activity recognition plays a central role in the development of intelligent systems for video surveillance, public security, health care and home monitoring, where detection and recognition of activities can improve the quality of life and security of humans. Typically, automated, intuitive and real-time systems are required to recognize human activities and identify accurately unusual behaviors in order to prevent dangerous situations. In this work, we explore the combination of three modalities (RGB, depth and skeleton data) to design a robust multi-modal framework for vision-based human activity recognition. Especially, spatial information, body shape/posture and temporal evolution of actions are highlighted using illustrative representations obtained from a combination of dynamic RGB images, dynamic depth images and skeleton data representations. Therefore, each video is represented with three images that summarize the ongoing action. Our framework takes advantage of transfer learning from pre trained models to extract significant features from these newly created images. Next, we fuse extracted features using Canonical Correlation Analysis and train a Long Short-Term Memory network to classify actions from visual descriptive images. Experimental results demonstrated the reliability of our feature-fusion framework that allows us to capture highly significant features and enables us to achieve the state-of-the-art performance on the public UTD-MHAD and NTU RGB+D datasets.

Attentive Visual Semantic Specialized Network for Video Captioning

Jesus Perez-Martin, Benjamin Bustos, Jorge Pérez

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Auto-TLDR; Adaptive Visual Semantic Specialized Network for Video Captioning

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As an essential high-level task of video understanding topic, automatically describing a video with natural language has recently gained attention as a fundamental challenge in computer vision. Previous models for video captioning have several limitations, such as the existence of gaps in current semantic representations and the inexpressibility of the generated captions. To deal with these limitations, in this paper, we present a new architecture that we callAttentive Visual Semantic Specialized Network(AVSSN), which is an encoder-decoder model based on our Adaptive Attention Gate and Specialized LSTM layers. This architecture can selectively decide when to use visual or semantic information into the text generation process. The adaptive gate makes the decoder to automatically select the relevant information for providing a better temporal state representation than the existing decoders. Besides, the model is capable of learning to improve the expressiveness of generated captions attending to their length, using a sentence-length-related loss function. We evaluate the effectiveness of the proposed approach on the Microsoft Video Description(MSVD) and the Microsoft Research Video-to-Text (MSR-VTT) datasets, achieving state-of-the-art performance with several popular evaluation metrics: BLEU-4, METEOR, CIDEr, and ROUGE_L.

Deep Convolutional Embedding for Digitized Painting Clustering

Giovanna Castellano, Gennaro Vessio

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

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

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

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

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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.

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

Jinting Wu, Yujia Zhang, Xiao-Guang Zhao

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

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

A Multi-Task Neural Network for Action Recognition with 3D Key-Points

Rongxiao Tang, Wang Luyang, Zhenhua Guo

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Auto-TLDR; Multi-task Neural Network for Action Recognition and 3D Human Pose Estimation

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Action recognition and 3D human pose estimation are the fundamental problems in computer vision and closely related. In this work, we propose a multi-task neural network for action recognition and 3D human pose estimation. The results of the previous methods are still error-prone especially when tested against the images taken in-the-wild, leading error results in action recognition. To solve this problem, we propose a principled approach to generate high quality 3D pose ground truth given any in-the-wild image with a person inside. We achieve this by first devising a novel stereo inspired neural network to directly map any 2D pose to high quality 3D counterpart. Based on the high-quality 3D labels, we carefully design the multi-task framework for action recognition and 3D human pose estimation. The proposed architecture can utilize the shallow, deep features of the images, and the in-the-wild 3D human key-points to guide a more precise result. High quality 3D key-points can fully reflect the morphological features of motions, thus boosting the performance on action recognition. Experiments demonstrate that 3D pose estimation leads to significantly higher performance on action recognition than separated learning. We also evaluate the generalization ability of our method both quantitatively and qualitatively. The proposed architecture performs favorably against the baseline 3D pose estimation methods. In addition, the reported results on Penn Action and NTU datasets demonstrate the effectiveness of our method on the action recognition task.

One-Shot Representational Learning for Joint Biometric and Device Authentication

Sudipta Banerjee, Arun Ross

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

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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\%.

Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition

Mirco Planamente, Andrea Bottino, Barbara Caputo

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Auto-TLDR; A Single Stream Architecture for Egocentric Action Recognition from the First-Person Point of View

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Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from the first-person point of view. An open challenge in egocentric action recognition is that videos lack detailed information about the main actor's pose and thus tend to record only parts of the movement when focusing on manipulation tasks. Thus, the amount of information about the action itself is limited, making crucial the understanding of the manipulated objects and their context. Many previous works addressed this issue with two-stream architectures, where one stream is dedicated to modeling the appearance of objects involved in the action, and another to extracting motion features from optical flow. In this paper, we argue that learning features jointly from these two information channels is beneficial to capture the spatio-temporal correlations between the two better. To this end, we propose a single stream architecture able to do so, thanks to the addition of a self-supervised block that uses a pretext motion prediction task to intertwine motion and appearance knowledge. Experiments on several publicly available databases show the power of our approach.

Pose Variation Adaptation for Person Re-Identification

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

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

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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.

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

Toby Breckon, Aishah Alsehaim

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

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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.

RGB-Infrared Person Re-Identification Via Image Modality Conversion

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

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

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

Adaptive L2 Regularization in Person Re-Identification

Xingyang Ni, Liang Fang, Heikki Juhani Huttunen

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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.

Classifying Eye-Tracking Data Using Saliency Maps

Shafin Rahman, Sejuti Rahman, Omar Shahid, Md. Tahmeed Abdullah, Jubair Ahmed Sourov

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Auto-TLDR; Saliency-based Feature Extraction for Automatic Classification of Eye-tracking Data

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A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-fixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD [1], Age Prediction [2], and Visual Perceptual Task [3] dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods [2],[4], [5] by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.

Person Recognition with HGR Maximal Correlation on Multimodal Data

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

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

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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.

DAIL: Dataset-Aware and Invariant Learning for Face Recognition

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

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

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

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

Rodolfo Quispe, Helio Pedrini

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

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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.

Single View Learning in Action Recognition

Gaurvi Goyal, Nicoletta Noceti, Francesca Odone

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Auto-TLDR; Cross-View Action Recognition Using Domain Adaptation for Knowledge Transfer

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Viewpoint is an essential aspect of how an action is visually perceived, with the motion appearing substantially different for some viewpoint pairs. Data driven action recognition algorithms compensate for this by including a variety of viewpoints in their training data, adding to the cost of data acquisition as well as training. We propose a novel methodology that leverages deeply pretrained features to learn actions from a single viewpoint using domain adaptation for knowledge transfer. We demonstrate the effectiveness of this pipeline on 3 different datasets: IXMAS, MoCA and NTU RGBD+, and compare with both classical and deep learning methods. Our method requires low training data and demonstrates unparalleled cross-view action recognition accuracies for single view learning.

A Two-Stream Recurrent Network for Skeleton-Based Human Interaction Recognition

Qianhui Men, Edmond S. L. Ho, Shum Hubert P. H., Howard Leung

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Auto-TLDR; Two-Stream Recurrent Neural Network for Human-Human Interaction Recognition

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This paper addresses the problem of recognizing human-human interaction from skeletal sequences. Existing methods are mainly designed to classify single human action. Many of them simply stack the movement features of two characters to deal with human interaction, while neglecting the abundant relationships between characters. In this paper, we propose a novel two-stream recurrent neural network by adopting the geometric features from both single actions and interactions to describe the spatial correlations with different discriminative abilities. The first stream is constructed under pairwise joint distance (PJD) in a fully-connected mesh to categorize the interactions with explicit distance patterns. To better distinguish similar interactions, in the second stream, we combine PJD with the spatial features from individual joint positions using graph convolutions to detect the implicit correlations among joints, where the joint connections in the graph are adaptive for flexible correlations. After spatial modeling, each stream is fed to a bi-directional LSTM to encode two-way temporal properties. To take advantage of the diverse discriminative power of the two streams, we come up with a late fusion algorithm to combine their output predictions concerning information entropy. Experimental results show that the proposed framework achieves state-of-the-art performance on 3D and comparable performance on 2D interaction datasets. Moreover, the late fusion results demonstrate the effectiveness of improving the recognition accuracy compared with single streams.

Age Gap Reducer-GAN for Recognizing Age-Separated Faces

Daksha Yadav, Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore

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Auto-TLDR; Generative Adversarial Network for Age-separated Face Recognition

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In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework which combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.

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

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

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

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

Feature Pyramid Hierarchies for Multi-Scale Temporal Action Detection

Jiayu He, Guohui Li, Jun Lei

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Auto-TLDR; Temporal Action Detection using Pyramid Hierarchies and Multi-scale Feature Maps

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Temporal action detection is a challenging but promising task in video content analysis. It is in great demand in the field of public safety. The main difficulty of the task is precisely localizing activities in the video especially those short duration activities. And most of the existing methods can not achieve a satisfactory detection result. Our method addresses a key point to improve detection accuracy, which is to use multi-scale feature maps for regression and classification. In this paper, we introduce a novel network based on classification following proposal framework. In our network, a 3D feature pyramid hierarchies is built to enhance the ability of detecting short duration activities. The input RGB/Flow frames are first encoded by a 3D feature pyramid hierarchies, and this subnet produces multi-level feature maps. Then temporal proposal subnet uses these features to pick out proposals which might contain activity segments. Finally a pyramid region of interest (RoI) pooling pipeline and two fully connected layers reuse muti-level feature maps to refine the temporal boundaries of proposals and classify them. We use late feature fusion scheme to combine RGB and Flow information. The network is trained end-to-end and we evaluate it in THUMOS'14 dataset. Our network achieves a good result among typical methods. A further ablation test demonstrate that pyramid hierarchies is effective to improve detecting short duration activity segments.

Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification

Federico Pollastri, Juan Maroñas, Federico Bolelli, Giulia Ligabue, Roberto Paredes, Riccardo Magistroni, Costantino Grana

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Auto-TLDR; A Probabilistic Convolutional Neural Network for Immunofluorescence Classification in Renal Biopsy

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With this work we tackle immunofluorescence classification in renal biopsy, employing state-of-the-art Convolutional Neural Networks. In this setting, the aim of the probabilistic model is to assist an expert practitioner towards identifying the location pattern of antibody deposits within a glomerulus. Since modern neural networks often provide overconfident outputs, we stress the importance of having a reliable prediction, demonstrating that Temperature Scaling, a recently introduced re-calibration technique, can be successfully applied to immunofluorescence classification in renal biopsy. Experimental results demonstrate that the designed model yields good accuracy on the specific task, and that Temperature Scaling is able to provide reliable probabilities, which are highly valuable for such a task given the low inter-rater agreement.

Progressive Learning Algorithm for Efficient Person Re-Identification

Zhen Li, Hanyang Shao, Liang Niu, Nian Xue

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

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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.

Using Scene Graphs for Detecting Visual Relationships

Anurag Tripathi, Siddharth Srivastava, Brejesh Lall, Santanu Chaudhury

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Auto-TLDR; Relationship Detection using Context Aligned Scene Graph Embeddings

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In this paper we solve the problem of detecting relationships between pairs of objects in an image. We develop spatially aware word embeddings using scene graphs and use joint feature representations containing visual, spatial and semantic embeddings from the input images to train a deep network on the task of relationship detection. Further, we propose to utilize context aligned scene graph embeddings from the train set, without requiring explicit availability of scene graphs at test time. We show that the proposed method outperforms the state-of-the-art methods for predicate detection and provides competing results on relationship detection. We also show the generalization ability of the proposed method by performing predictions under zero shot settings. Further, we also provide an exhaustive empirical evaluation on each component of the proposed network.