On the Robustness of 3D Human Pose Estimation

Zerui Chen, Yan Huang, Liang Wang

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Auto-TLDR; Robustness of 3D Human Pose Estimation Methods to Adversarial Attacks

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It is widely shown that Convolutional Neural Networks (CNNs) are vulnerable to adversarial examples on most recognition tasks, such as image classification and segmentation. However, few work studies the more complicated task -- 3D human pose estimation. This task often requires large-scale datasets, specialized network architectures, and it can be solved either from single-view RGB images or from multi-view RGB images. In this paper, we make the first attempt to investigate the robustness of current state-of-the-art 3D human pose estimation methods. To this end, we build four representative baseline models, where most of the current methods can be generally classified as one of them. Furthermore, we design targeted adversarial attacks to detect whether 3D pose estimators are robust to different camera parameters. For different types of methods, we present a comprehensive study of their robustness on the large-scale \emph{Human3.6M} benchmark. Our work shows that different methods vary significantly in their resistance to adversarial attacks. Through extensive experiments, we show that multi-view 3D pose estimators can be more vulnerable to adversarial examples. We believe that our efforts can shed light on future works to design more robust 3D human pose estimators.

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Auto-TLDR; 3D Human Pose Estimation for Multi-Human Videos with Occlusion

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Auto-TLDR; Unsupervised 3D Human Pose Estimation from 2D Videos Using Generative Adversarial Network

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Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

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Auto-TLDR; Robust Adversarial Defense with Visual Imperceptible Bound

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Auto-TLDR; Robustness of DNN-based STD methods against Adversarial Attacks

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Auto-TLDR; Sinkhorn Adversarial Training with Optimal Transport Theory

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Auto-TLDR; Learning-free 3D Human Pose Estimation from Inertial Measurement Unit Data

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Auto-TLDR; F-Mixup: A novel black-box attack in frequency domain for deep neural networks

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Auto-TLDR; A Generalized Defense Mechanism for Adversarial Attacks on Data Manifolds

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Despite their tremendous success in modelling highdimensional data manifolds, deep neural networks suffer from the threat of adversarial attacks - Existence of perceptually valid input-like samples obtained through careful perturbation that lead to degradation in the performance of the underlying model. Major concerns with existing defense mechanisms include non-generalizability across different attacks, models and large inference time. In this paper, we propose a generalized defense mechanism capitalizing on the expressive power of regularized latent space based generative models. We design an adversarial filter, devoid of access to classifier and adversaries, which makes it usable in tandem with any classifier. The basic idea is to learn a Lipschitz constrained mapping from the data manifold, incorporating adversarial perturbations, to a quantized latent space and re-map it to the true data manifold. Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference. We demonstrate the efficacy of the proposed formulation in providing resilience against multiple attack types (black and white box) and methods, while being almost real-time. Our experiments show that the proposed method surpasses the stateof-the-art techniques in several cases.

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Yufan Hou, Lixin Zou, Weidong Liu

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Auto-TLDR; Task-based Adversarial Focal Loss for Few-shot Meta-Learner

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Adversarial robustness of machine learning has been widely studied in recent years, and a series of effective methods are proposed to resist adversarial attacks. However, less attention is paid to few-shot meta-learners which are much more vulnerable due to the lack of training samples. In this paper, we propose Task-based Adversarial Focal Loss (TAFL) to handle this tough challenge on a typical meta-learner called MAML. More concretely, we regard few-shot classification tasks as normal samples in learning models and apply focal loss mechanism on them. Our proposed method focuses more on adversarially fragile tasks, leading to improvement on overall model robustness. Results of extensive experiments on several benchmarks demonstrate that TAFL can effectively promote the performance of the meta-learner on adversarial examples with elaborately designed perturbations.

Weakly Supervised Body Part Segmentation with Pose Based Part Priors

Zhengyuan Yang, Yuncheng Li, Linjie Yang, Ning Zhang, Jiebo Luo

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Auto-TLDR; Weakly Supervised Body Part Segmentation Using Weak Labels

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Human body part segmentation refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part segmentation methods achieve good performances but require an enormous amount of effort to annotate part masks for training. In contrast to high annotation costs needed for a limited number of part mask annotations, a large number of weak labels such as poses and full body masks already exist and contain relevant information. Motivated by the possibility of using existing weak labels, we propose the first weakly supervised body part segmentation framework. The core idea is first converting the sparse weak labels such as keypoints to the initial estimate of body part masks, and then iteratively refine the part mask predictions. We name the initial part masks estimated from poses the "part priors". with sufficient extra weak labels, our weakly supervised framework achieves a comparable performance (62.0% mIoU) to the fully supervised method (63.6% mIoU) on the Pascal-Person-Part dataset. Furthermore, in the extended semi-supervised setting, the proposed framework outperforms the state-of-art methods. Moreover, we extend our proposed framework to other keypoint-supervised part segmentation tasks such as face parsing.

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Auto-TLDR; Adversarially Training for Robust Neural Networks against Adversarial Attacks

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In this paper, we investigate the potential effect of the adversarially training on the robustness of six advanced deep neural networks against a variety of targeted and non-targeted adversarial attacks. We firstly show that, the ResNet-56 model trained on the 2D representation of the discrete wavelet transform appended with the tonnetz chromagram outperforms other models in terms of recognition accuracy. Then we demonstrate the positive impact of adversarially training on this model as well as other deep architectures against six types of attack algorithms (white and black-box) with the cost of the reduced recognition accuracy and limited adversarial perturbation. We run our experiments on two benchmarking environmental sound datasets and show that without any imposed limitations on the budget allocations for the adversary, the fooling rate of the adversarially trained models can exceed 90%. In other words, adversarial attacks exist in any scales, but they might require higher adversarial perturbations compared to non-adversarially trained models.

A Delayed Elastic-Net Approach for Performing Adversarial Attacks

Brais Cancela, Veronica Bolon-Canedo, Amparo Alonso-Betanzos

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Auto-TLDR; Robustness of ImageNet Pretrained Models against Adversarial Attacks

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With the rise of the so-called Adversarial Attacks, there is an increased concern on model security. In this paper we present two different contributions: novel measures of robustness (based on adversarial attacks) and a novel adversarial attack. The key idea behind these metrics is to obtain a measure that could compare different architectures, with independence of how the input is preprocessed (robustness against different input sizes and value ranges). To do so, a novel adversarial attack is presented, performing a delayed elastic-net adversarial attack (constraints are only used whenever a successful adversarial attack is obtained). Experimental results show that our approach obtains state-of-the-art adversarial samples, in terms of minimal perturbation distance. Finally, a benchmark of ImageNet pretrained models is used to conduct experiments aiming to shed some light about which model should be selected whenever security is a role factor.

Partially Supervised Multi-Task Network for Single-View Dietary Assessment

Ya Lu, Thomai Stathopoulou, Stavroula Mougiakakou

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Auto-TLDR; Food Volume Estimation from a Single Food Image via Geometric Understanding and Semantic Prediction

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Food volume estimation is an essential step in the pipeline of dietary assessment and demands the precise depth estimation of the food surface and table plane. Existing methods based on computer vision require either multi-image input or additional depth maps, reducing convenience of implementation and practical significance. Despite the recent advances in unsupervised depth estimation from a single image, the achieved performance in the case of large texture-less areas needs to be improved. In this paper, we propose a network architecture that jointly performs geometric understanding (i.e., depth prediction and 3D plane estimation) and semantic prediction on a single food image, enabling a robust and accurate food volume estimation regardless of the texture characteristics of the target plane. For the training of the network, only monocular videos with semantic ground truth are required, while the depth map and 3D plane ground truth are no longer needed. Experimental results on two separate food image databases demonstrate that our method performs robustly on texture-less scenarios and is superior to unsupervised networks and structure from motion based approaches, while it achieves comparable performance to fully-supervised methods.

Beyond Cross-Entropy: Learning Highly Separable Feature Distributions for Robust and Accurate Classification

Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli

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Auto-TLDR; Gaussian class-conditional simplex loss for adversarial robust multiclass classifiers

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Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets.

StrongPose: Bottom-up and Strong Keypoint Heat Map Based Pose Estimation

Niaz Ahmad, Jongwon Yoon

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Auto-TLDR; StrongPose: A bottom-up box-free approach for human pose estimation and action recognition

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Adaptation of deep convolutional neural network has made revolutionary progress in human pose estimation, various applications in recent years have drawn considerable attention. However, prediction and localization of the keypoints in single and multi-person images are a challenging problem. Towards this purpose, we present a bottom-up box-free approach for the task of pose estimation and action recognition. We proposed a StrongPose system model that uses part-based modeling to tackle object-part associations. The model utilizes a convolution network that learns how to detect Strong Keypoints Heat Maps (SKHM) and predict their comparative displacements, enabling us to group keypoints into person pose instances. Further, we produce Body Heat Maps (BHM) with the help of keypoints which allows us to localize the human body in the picture. The StrongPose framework is based on fully-convolutional engineering and permits proficient inference, with runtime basically autonomous of the number of individuals display within the scene. Train and test on COCO data alone, our framework achieves COCO test-dev keypoint average precision of 0.708 using ResNet-101 and 0.725 using ResNet-152, which considerably outperforms all prior bottom-up pose estimation frameworks.

Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers

Yi Xiang Marcus Tan, Yuval Elovici, Alexander Binder

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Auto-TLDR; Adaptive Stochastic Networks for Adversarial Attacks

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Adversarial attacks have been a prevalent problem causing misclassification in machine learning models, with stochasticity being a promising direction towards greater robustness. However, stochastic networks frequently underperform compared to deterministic deep networks. In this work, we present a conceptually clear adaptive noise injection mechanism in combination with teacher-initialisation, which adjusts its degree of randomness dynamically through the computation of mini-batch statistics. This mechanism is embedded within a simple framework to obtain stochastic networks from existing deterministic networks. Our experiments show that our method is able to outperform prior baselines under white-box settings, exemplified through CIFAR-10 and CIFAR-100. Following which, we perform in-depth analysis on varying different components of training with our approach on the effects of robustness and accuracy, through the study of the evolution of decision boundary and trend curves of clean accuracy/attack success over differing degrees of stochasticity. We also shed light on the effects of adversarial training on a pre-trained network, through the lens of decision boundaries.

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.

DeepPear: Deep Pose Estimation and Action Recognition

Wen-Jiin Tsai, You-Ying Jhuang

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Auto-TLDR; Human Action Recognition Using RGB Video Using 3D Human Pose and Appearance Features

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Human action recognition has been a popular issue recently because it can be applied in many applications such as intelligent surveillance systems, human-robot interaction, and autonomous vehicle control. Human action recognition using RGB video is a challenging task because the learning of actions is easily affected by the cluttered background. To cope with this problem, the proposed method estimates 3D human poses first which can help remove the cluttered background and focus on the human body. In addition to the human poses, the proposed method also utilizes appearance features nearby the predicted joints to make our action prediction context-aware. Instead of using 3D convolutional neural networks as many action recognition approaches did, the proposed method uses a two-stream architecture that aggregates the results from skeleton-based and appearance-based approaches to do action recognition. Experimental results show that the proposed method achieved state-of-the-art performance on NTU RGB+D which is a largescale dataset for human action recognition.

MixedFusion: 6D Object Pose Estimation from Decoupled RGB-Depth Features

Hangtao Feng, Lu Zhang, Xu Yang, Zhiyong Liu

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Auto-TLDR; MixedFusion: Combining Color and Point Clouds for 6D Pose Estimation

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Estimating the 6D pose of objects is an important process for intelligent systems to achieve interaction with the real-world. As the RGB-D sensors become more accessible, the fusion-based methods have prevailed, since the point clouds provide complementary geometric information with RGB values. However, Due to the difference in feature space between color image and depth image, the network structures that directly perform point-to-point matching fusion do not effectively fuse the features of the two. In this paper, we propose a simple but effective approach, named MixedFusion. Different from the prior works, we argue that the spatial correspondence of color and point clouds could be decoupled and reconnected, thus enabling a more flexible fusion scheme. By performing the proposed method, more informative points can be mixed and fused with rich color features. Extensive experiments are conducted on the challenging LineMod and YCB-Video datasets, show that our method significantly boosts the performance without introducing extra overheads. Furthermore, when the minimum tolerance of metric narrows, the proposed approach performs better for the high-precision demands.

JUMPS: Joints Upsampling Method for Pose Sequences

Lucas Mourot, Francois Le Clerc, Cédric Thébault, Pierre Hellier

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Auto-TLDR; JUMPS: Increasing the Number of Joints in 2D Pose Estimation and Recovering Occluded or Missing Joints

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Human Pose Estimation is a low-level task useful for surveillance, human action recognition, and scene understanding at large. It also offers promising perspectives for the animation of synthetic characters. For all these applications, and especially the latter, estimating the positions of many joints is desirable for improved performance and realism. To this purpose, we propose a novel method called JUMPS for increasing the number of joints in 2D pose estimates and recovering occluded or missing joints. We believe this is the first attempt to address the issue. We build on a deep generative model that combines a GAN and an encoder. The GAN learns the distribution of high-resolution human pose sequences, the encoder maps the input low-resolution sequences to its latent space. Inpainting is obtained by computing the latent representation whose decoding by the GAN generator optimally matches the joints locations at the input. Post-processing a 2D pose sequence using our method provides a richer representation of the character motion. We show experimentally that the localization accuracy of the additional joints is on average on par with the original pose estimates.

CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection

Shengnan Hu, Yang Zhang, Sumit Laha, Ankit Sharma, Hassan Foroosh

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Auto-TLDR; Contextual camouflage attack for object detection

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Deep neural network based object detection has become the cornerstone of many real-world applications. Along with this success comes concerns about its vulnerability to malicious attacks. To gain more insight into this issue, we propose a contextual camouflage attack (CCA for short) algorithm to influence the performance of object detectors. In this paper, we use an evolutionary search strategy and adversarial machine learning in interactions with a photo-realistic simulated environment to find camouflage patterns that are effective over a huge variety of object locations, camera poses, and lighting conditions. The proposed camouflages are validated effective to most of the state-of-the-art object detectors.

Joint Face Alignment and 3D Face Reconstruction with Efficient Convolution Neural Networks

Keqiang Li, Huaiyu Wu, Xiuqin Shang, Zhen Shen, Gang Xiong, Xisong Dong, Bin Hu, Fei-Yue Wang

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Auto-TLDR; Mobile-FRNet: Efficient 3D Morphable Model Alignment and 3D Face Reconstruction from a Single 2D Facial Image

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3D face reconstruction from a single 2D facial image is a challenging and concerned problem. Recent methods based on CNN typically aim to learn parameters of 3D Morphable Model (3DMM) from 2D images to render face alignment and 3D face reconstruction. Most algorithms are designed for faces with small, medium yaw angles, which is extremely challenging to align faces in large poses. At the same time, they are not efficient usually. The main challenge is that it takes time to determine the parameters accurately. In order to address this challenge with the goal of improving performance, this paper proposes a novel and efficient end-to-end framework. We design an efficient and lightweight network model combined with Depthwise Separable Convolution and Muti-scale Representation, Lightweight Attention Mechanism, named Mobile-FRNet. Simultaneously, different loss functions are used to constrain and optimize 3DMM parameters and 3D vertices during training to improve the performance of the network. Meanwhile, extensive experiments on the challenging datasets show that our method significantly improves the accuracy of face alignment and 3D face reconstruction. The model parameters and complexity of our method are also improved greatly.

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.

Novel View Synthesis from a 6-DoF Pose by Two-Stage Networks

Xiang Guo, Bo Li, Yuchao Dai, Tongxin Zhang, Hui Deng

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Auto-TLDR; Novel View Synthesis from a 6-DoF Pose Using Generative Adversarial Network

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Novel view synthesis is a challenging problem in 3D vision and robotics. Different from the existing works, which need the reference images or 3D model, we propose a novel paradigm to this problem. That is, we synthesize the novel view from a 6-DoF pose directly. Although this setting is the most straightforward way, there are few works addressing it. While, our experiments demonstrate that, with a concise CNN, we could get a meaningful parametric model which could reconstruct the correct scenery images only from the 6-DoF pose. To this end, we propose a two-stage learning strategy, which consists of two consecutive CNNs: GenNet and RefineNet. The GenNet generates a coarse image from a camera pose. The RefineNet is a generative adversarial network that could refine the coarse image. In this way, we decouple the geometric relationship mapping and texture detail rendering. Extensive experiments conducted on the public datasets prove the effectiveness of our method. We believe this paradigm is of high research and application value and could be an important direction in novel view synthesis. We will share our code after the acceptance of this work.

Silhouette Body Measurement Benchmarks

Song Yan, Johan Wirta, Joni-Kristian Kamarainen

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Auto-TLDR; BODY-fit: A Realistic 3D Body Measurement Dataset for Anthropometric Measurement

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Anthropometric body measurements are important for industrial design, garment fitting, medical diagnosis and ergonomics. A number of methods have been proposed to estimate the body measurements from images, but progress has been slow due to the lack of realistic and publicly available datasets. The existing works train and test on silhouettes of 3D body meshes obtained by fitting a human body model to the commercial CAESAR scans. In this work, we introduce the BODY-fit dataset that contains fitted meshes of 2,675 female and 1,474 male 3D body scans. We unify evaluation on the CAESAR-fit and BODY-fit datasets by computing body measurements from geodesic surface paths as the ground truth and by generating two-view silhouette images. We also introduce BODY-rgb - a realistic dataset of 86 male and 108 female subjects captured with an RGB camera and manually tape measured ground truth. We propose a simple yet effective deep CNN architecture as a baseline method which obtains competitive accuracy on the three datasets.

Verifying the Causes of Adversarial Examples

Honglin Li, Yifei Fan, Frieder Ganz, Tony Yezzi, Payam Barnaghi

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Auto-TLDR; Exploring the Causes of Adversarial Examples in Neural Networks

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The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial examples falls behind studies on attacks and defenses. In this paper, we present a collection of potential causes of adversarial examples and verify (or partially verify) them through carefully-designed controlled experiments. The major causes of adversarial examples include model linearity, one-sum constraint, and geometry of the categories. To control the effect of those causes, multiple techniques are applied such as $L_2$ normalization, replacement of loss functions, construction of reference datasets, and novel models using multi-layer perceptron probabilistic neural networks (MLP-PNN) and density estimation (DE). Our experiment results show that geometric factors tend to be more direct causes and statistical factors magnify the phenomenon, especially for assigning high prediction confidence. We hope this paper will inspire more studies to rigorously investigate the root causes of adversarial examples, which in turn provide useful guidance on designing more robust models.

Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction

Ziyang Song, Ziyi Yin, Zejian Yuan, Chong Zhang, Wanchao Chi, Yonggen Ling, Shenghao Zhang

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Auto-TLDR; Attention-Oriented Multi-Level Network for Action Recognition in Interaction Scenes

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Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction. In this paper, we deeply explore the characteristics of the action recognition task in interaction scenes and propose an attention-oriented multi-level network framework to meet the need for real-time interaction. Specifically, a Pre-Attention network is employed to roughly focus on the interactor in the scene at low resolution firstly and then perform fine-grained pose estimation at high resolution. The other compact CNN receives the extracted skeleton sequence as input for action recognition, utilizing attention-like mechanisms to capture local spatial-temporal patterns and global semantic information effectively. To evaluate our approach, we construct a new action dataset specially for the recognition task in interaction scenes. Experimental results on our dataset and high efficiency (112 fps at 640 x 480 RGBD) on the mobile computing platform (Nvidia Jetson AGX Xavier) demonstrate excellent applicability of our method on action recognition in real-time human-robot interaction.

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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We propose a novel joint framework for 3D face reconstruction (3DFR) that integrates facial attribute estimation (FAE) as an auxiliary task. One of the essential problems of 3DFR is to extract semantic facial features (e.g., Big Nose, High Cheekbones, and Asian) from in-the-wild 2D images, which is inherently involved with FAE. These two tasks, though heterogeneous, are highly relevant to each other. To achieve this, we leverage a Convolutional Neural Network to extract shared facial representations for both shape decoder and attribute classifier. We further develop an in-batch hybrid-task training scheme that enables our model to learn from heterogeneous facial datasets jointly within a mini-batch. Thanks to the joint loss that provides supervision from both 3DFR and FAE domains, our model learns the correlations between 3D shapes and facial attributes, which benefit both feature extraction and shape inference. Quantitative evaluation and qualitative visualization results confirm the effectiveness and robustness of our joint framework.

Towards Efficient 3D Point Cloud Scene Completion Via Novel Depth View Synthesis

Haiyan Wang, Liang Yang, Xuejian Rong, Ying-Li Tian

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Auto-TLDR; 3D Point Cloud Completion with Depth View Synthesis and Depth View synthesis

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3D point cloud completion has been a long-standing challenge at scale, and corresponding per-point supervised training strategies suffered from the cumbersome annotations. 2D supervision has recently emerged as a promising alternative for 3D tasks, but specific approaches for 3D point cloud completion still remain to be explored. To overcome these limitations, we propose an end-to-end method that directly lifts a single depth map to a completed point cloud. With one depth map as input, a multi-way novel depth view synthesis network (NDVNet) is designed to infer coarsely completed depth maps under various viewpoints. Meanwhile, a geometric depth perspective rendering module is introduced to utilize the raw input depth map to generate a re-projected depth map for each view. Therefore, the two parallelly generated depth maps for each view are further concatenated and refined by a depth completion network (DCNet). The final completed point cloud is fused from all refined depth views. Experimental results demonstrate the effectiveness of our proposed approach composed of aforementioned components, to produce high-quality state-of-the-art results on the popular SUNCG benchmark.

Object-Oriented Map Exploration and Construction Based on Auxiliary Task Aided DRL

Junzhe Xu, Jianhua Zhang, Shengyong Chen, Honghai Liu

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Auto-TLDR; Auxiliary Task Aided Deep Reinforcement Learning for Environment Exploration by Autonomous Robots

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Environment exploration by autonomous robots through deep reinforcement learning (DRL) based methods has attracted more and more attention. However, existing methods usually focus on robot navigation to single or multiple fixed goals, while ignoring the perception and construction of external environments. In this paper, we propose a novel environment exploration task based on DRL, which requires a robot fast and completely perceives all objects of interest, and reconstructs their poses in a global environment map, as much as the robot can do. To this end, we design an auxiliary task aided DRL model, which is integrated with the auxiliary object detection and 6-DoF pose estimation components. The outcome of auxiliary tasks can improve the learning speed and robustness of DRL, as well as the accuracy of object pose estimation. Comprehensive experimental results on the indoor simulation platform AI2-THOR have shown the effectiveness and robustness of our method.

Attack-Agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning

Matthew Watson, Noura Al Moubayed

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Auto-TLDR; Explainability-based Detection of Adversarial Samples on EHR and Chest X-Ray Data

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Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making incorrect predictions. In this work, we propose an explainability-based method for the accurate detection of adversarial samples on two datasets with different complexity and properties: Electronic Health Record (EHR) and chest X-ray (CXR) data. On the MIMIC-III and Henan-Renmin EHR datasets, we report a detection accuracy of 77% against the Longitudinal Adversarial Attack. On the MIMIC-CXR dataset, we achieve an accuracy of 88%; significantly improving on the state of the art of adversarial detection in both datasets by over 10% in all settings. We propose an anomaly detection based method using explainability techniques to detect adversarial samples which is able to generalise to different attack methods without a need for retraining.

6D Pose Estimation with Correlation Fusion

Yi Cheng, Hongyuan Zhu, Ying Sun, Cihan Acar, Wei Jing, Yan Wu, Liyuan Li, Cheston Tan, Joo-Hwee Lim

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Auto-TLDR; Intra- and Inter-modality Fusion for 6D Object Pose Estimation with Attention Mechanism

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6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illumination, so it is important to complement them with depth information. However, existing methods using RGB-D data cannot adequately exploit consistent and complementary information between RGB and depth modalities. In this paper, we present a novel method to effectively consider the correlation within and across both modalities with attention mechanism to learn discriminative and compact multi-modal features. Then, effective fusion strategies for intra- and inter-correlation modules are explored to ensure efficient information flow between RGB and depth. To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation. The experimental results show that our method can achieve the state-of-the-art performance on LineMOD and YCBVideo dataset. We also demonstrate that the proposed method can benefit a real-world robot grasping task by providing accurate object pose estimation.