bluuebunny/crossref_metadata_2025 huggingface dataset:
Information Bottleneck Approach to Spatial Attention Learning
Authors: Lai Qiuxia , Li Yu , Zeng Ailing , Liu Minhao , Sun Hanqiu , Xu Qiang
Published: August 2021

The selective visual attention mechanism in the human visual system (HVS) restricts the amount of information to reach visual awareness for perceiving natural scenes, allowing near real-time information processing with limited computational capacity. This kind of selectivity acts as an ‘Information Bottleneck (IB)’, which seeks a trade-off between information compression and predictive accuracy. However, such information constraints are rarely explored in the attention mechanism for deep neural networks (DNNs). In this paper, we propose an IB-inspired spatial attention module for DNN structures built for visual recognition. The module takes as input an intermediate representation of the input image, and outputs a variational 2D attention map that minimizes the mutual information (MI) between the attention-modulated representation and the input, while maximizing the MI between the attention-modulated representation and the task label. To further restrict the information bypassed by the attention map, we quantize the continuous attention scores to a set of learnable anchor values during training. Extensive experiments show that the proposed IB-inspired spatial attention mechanism can yield attention maps that neatly highlight the regions of interest while suppressing backgrounds, and bootstrap standard DNN structures for visual recognition tasks (e.g., image classification, fine-grained recognition, cross-domain classification). The attention maps are interpretable for the decision making of the DNNs as verified in the experiments. Our code is available at this https URL.
Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting
Authors: Li Ang , Ke Qiuhong , Ma Xingjun , Weng Haiqin , Zong Zhiyuan , Xue Feng , Zhang Rui
Published: August 2021

Deep image inpainting aims to restore damaged or missing regions in an image with realistic contents. While having a wide range of applications such as object removal and image recovery, deep inpainting techniques also have the risk of being manipulated for image forgery. A promising countermeasure against such forgeries is deep inpainting detection, which aims to locate the inpainted regions in an image. In this paper, we make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well when detecting different deep inpainting methods. To this end, we first propose a novel data generation approach to generate a universal training dataset, which imitates the noise discrepancies exist in real versus inpainted image contents to train universal detectors. We then design a Noise-Image Cross-fusion Network (NIX-Net) to effectively exploit the discriminative information contained in both the images and their noise patterns. We empirically show, on multiple benchmark datasets, that our approach outperforms existing detection methods by a large margin and generalize well to unseen deep inpainting techniques. Our universal training dataset can also significantly boost the generalizability of existing detection methods.
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement
Authors: Li Jie , Ding Laiyan , Huang Rui
Published: August 2021

3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using positively correlated high-level features. Current methods use 2D features extracted from early-fused RGB-D images for 2D segmentation to improve 3D scene completion. We argue that this sequential scheme does not ensure these two tasks fully benefit each other, and present an Iterative Mutual Enhancement Network (IMENet) to solve them jointly, which interactively refines the two tasks at the late prediction stage. Specifically, two refinement modules are developed under a unified framework for the two tasks. The first is a 2D Deformable Context Pyramid (DCP) module, which receives the projection from the current 3D predictions to refine the 2D predictions. In turn, a 3D Deformable Depth Attention (DDA) module is proposed to leverage the reprojected results from 2D predictions to update the coarse 3D predictions. This iterative fusion happens to the stable high-level features of both tasks at a late stage. Extensive experiments on NYU and NYUCAD datasets verify the effectiveness of the proposed iterative late fusion scheme, and our approach outperforms the state of the art on both 3D semantic scene completion and 2D semantic segmentation.
Medical Image Segmentation using Squeeze-and-Expansion Transformers
Authors: Li Shaohua , Sui Xiuchao , Luo Xiangde , Xu Xinxing , Liu Yong , Goh Rick
Published: August 2021

Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transformers, which have unlimited "effective receptive fields" even at high feature resolutions. The core of Segtran is a novel Squeeze-and-Expansion transformer: a squeezed attention block regularizes the self attention of transformers, and an expansion block learns diversified representations. Additionally, we propose a new positional encoding scheme for transformers, imposing a continuity inductive bias for images. Experiments were performed on 2D and 3D medical image segmentation tasks: optic disc/cup segmentation in fundus images (REFUGE'20 challenge), polyp segmentation in colonoscopy images, and brain tumor segmentation in MRI scans (BraTS'19 challenge). Compared with representative existing methods, Segtran consistently achieved the highest segmentation accuracy, and exhibited good cross-domain generalization capabilities.
Learning 3-D Human Pose Estimation from Catadioptric Videos
Authors: Liu Chenchen , Li Yongzhi , Ma Kangqi , Zhang Duo , Bao Peijun , Mu Yadong
Published: August 2021

3-D human pose estimation is a crucial step for understanding human actions. However, reliably capturing precise 3-D position of human joints is non-trivial and tedious. Current models often suffer from the scarcity of high-quality 3-D annotated training data. In this work, we explore a novel way of obtaining gigantic 3-D human pose data without manual annotations. In catedioptric videos (\emph{e.g.}, people dance before a mirror), the camera records both the original and mirrored human poses, which provides cues for estimating 3-D positions of human joints. Following this idea, we crawl a large-scale Dance-before-Mirror (DBM) video dataset, which is about 24 times larger than existing Human3.6M benchmark. Our technical insight is that, by jointly harnessing the epipolar geometry and human skeleton priors, 3-D joint estimation can boil down to an optimization problem over two sets of 2-D estimations. To our best knowledge, this represents the first work that collects high-quality 3-D human data via catadioptric systems. We have conducted comprehensive experiments on cross-scenario pose estimation and visualization analysis. The results strongly demonstrate the usefulness of our proposed DBM human poses.
Bipartite Matching for Crowd Counting with Point Supervision
Authors: Liu Hao , Zhao Qiang , Ma Yike , Dai Feng
Published: August 2021

For crowd counting task, it has been demonstrated that imposing Gaussians to point annotations hurts generalization performance. Several methods attempt to utilize point annotations as supervision directly. And they have made significant improvement compared with density-map based methods. However, these point based methods ignore the inevitable annotation noises and still suffer from low robustness to noisy annotations. To address the problem, we propose a bipartite matching based method for crowd counting with only point supervision (BM-Count). In BM-Count, we select a subset of most similar pixels from the predicted density map to match annotated pixels via bipartite matching. Then loss functions can be defined based on the matching pairs to alleviate the bad effect caused by those annotated dots with incorrect positions. Under the noisy annotations, our method reduces MAE and RMSE by 9% and 11.2% respectively. Moreover, we propose a novel ranking distribution learning framework to address the imbalanced distribution problem of head counts, which encodes the head counts as classification distribution in the ranking domain and refines the estimated count map in the continuous domain. Extensive experiments on four datasets show that our method achieves state-of-the-art performance and performs better crowd localization.
One-Shot Affordance Detection
Authors: Luo Hongchen , Zhai Wei , Zhang Jing , Cao Yang , Tao Dacheng
Published: August 2021

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection (OS-AD) network that firstly estimates the purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OS-AD can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k images from 31 affordance and 72 object categories. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is at ProjectPage.
Point-based Acoustic Scattering for Interactive Sound Propagation via Surface Encoding
Authors: Meng Hsien-Yu , Tang Zhenyu , Manocha Dinesh
Published: August 2021

We present a novel geometric deep learning method to compute the acoustic scattering properties of geometric objects. Our learning algorithm uses a point cloud representation of objects to compute the scattering properties and integrates them with ray tracing for interactive sound propagation in dynamic scenes. We use discrete Laplacian-based surface encoders and approximate the neighborhood of each point using a shared multi-layer perceptron. We show that our formulation is permutation invariant and present a neural network that computes the scattering function using spherical harmonics. Our approach can handle objects with arbitrary topologies and deforming models, and takes less than 1ms per object on a commodity GPU. We have analyzed the accuracy and perform validation on thousands of unseen 3D objects and highlight the benefits over other point-based geometric deep learning methods. To the best of our knowledge, this is the first real-time learning algorithm that can approximate the acoustic scattering properties of arbitrary objects with high accuracy.
Attention-based Pyramid Dilated Lattice Network for Blind Image Denoising
Authors: Nikzad Mohammad , Gao Yongsheng , Zhou Jun
Published: August 2021

Though convolutional neural networks (CNNs) with residual and dense aggregations have obtained much attention in image denoising, they are incapable of exploiting different levels of contextual information at every convolutional unit in order to infer different levels of noise components with a single model. In this paper, to overcome this shortcoming we present a novel attention-based pyramid dilated lattice (APDL) architecture and investigate its capability for blind image denoising. The proposed framework can effectively harness the advantages of residual and dense aggregations to achieve a great trade-off between performance, parameter efficiency, and test time. It also employs a novel pyramid dilated convolution strategy to effectively capture contextual information corresponding to different noise levels through the training of a single model. Our extensive experimental investigation verifies the effectiveness and efficiency of the APDL architecture for image denoising as well as JPEG artifacts suppression tasks.
Few-shot Neural Human Performance Rendering from Sparse RGBD Videos
Authors: Pang Anqi , Chen Xin , Luo Haimin , Wu Minye , Yu Jingyi , Xu Lan
Published: August 2021

Recent neural rendering approaches for human activities achieve remarkable view synthesis results, but still rely on dense input views or dense training with all the capture frames, leading to deployment difficulty and inefficient training overload. However, existing advances will be ill-posed if the input is both spatially and temporally sparse. To fill this gap, in this paper we propose a few-shot neural human rendering approach (FNHR) from only sparse RGBD inputs, which exploits the temporal and spatial redundancy to generate photo-realistic free-view output of human activities. Our FNHR is trained only on the key-frames which expand the motion manifold in the input sequences. We introduce a two-branch neural blending to combine the neural point render and classical graphics texturing pipeline, which integrates reliable observations over sparse key-frames. Furthermore, we adopt a patch-based adversarial training process to make use of the local redundancy and avoids over-fitting to the key-frames, which generates fine-detailed rendering results. Extensive experiments demonstrate the effectiveness of our approach to generate high-quality free view-point results for challenging human performances under the sparse setting.
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking
Authors: Peng Jinlong , Jiang Zhengkai , Gu Yueyang , Wu Yang , Wang Yabiao , Tai Ying , Wang Chengjie , Lin Weiyao
Published: August 2021

Recently, most siamese network based trackers locate targets via object classification and bounding-box regression. Generally, they select the bounding-box with maximum classification confidence as the final prediction. This strategy may miss the right result due to the accuracy misalignment between classification and regression. In this paper, we propose a novel siamese tracking algorithm called SiamRCR, addressing this problem with a simple, light and effective solution. It builds reciprocal links between classification and regression branches, which can dynamically re-weight their losses for each positive sample. In addition, we add a localization branch to predict the localization accuracy, so that it can work as the replacement of the regression assistance link during inference. This branch makes the training and inference more consistent. Extensive experimental results demonstrate the effectiveness of SiamRCR and its superiority over the state-of-the-art competitors on GOT-10k, LaSOT, TrackingNet, OTB-2015, VOT-2018 and VOT-2019. Moreover, our SiamRCR runs at 65 FPS, far above the real-time requirement.
Learning Visual Words for Weakly-Supervised Semantic Segmentation
Authors: Ru Lixiang , Du Bo , Wu Chen
Published: August 2021

Current weakly-supervised semantic segmentation (WSSS) methods with image-level labels mainly adopt class activation maps (CAM) to generate the initial pseudo labels. However, CAM usually only identifies the most discriminative object extents, which is attributed to the fact that the network doesn't need to discover the integral object to recognize image-level labels. In this work, to tackle this problem, we proposed to simultaneously learn the image-level labels and local visual word labels. Specifically, in each forward propagation, the feature maps of the input image will be encoded to visual words with a learnable codebook. By enforcing the network to classify the encoded fine-grained visual words, the generated CAM could cover more semantic regions. Besides, we also proposed a hybrid spatial pyramid pooling module that could preserve local maximum and global average values of feature maps, so that more object details and less background were considered. Based on the proposed methods, we conducted experiments on the PASCAL VOC 2012 dataset. Our proposed method achieved 67.2% mIoU on the val set and 67.3% mIoU on the test set, which outperformed recent state-of-the-art methods.
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Authors: Sun Yujia , Chen Geng , Zhou Tao , Zhang Yi , Liu Nian
Published: August 2021

Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD. In this paper, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task. Specifically, we propose an Attention-induced Cross-level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients. The fused features are then fed to the proposed Dual-branch Global Context Module (DGCM), which yields multi-scale feature representations for exploiting rich global context information. In C2F-Net, the two modules are conducted on high-level features using a cascaded manner. Extensive experiments on three widely used benchmark datasets demonstrate that our C2F-Net is an effective COD model and outperforms state-of-the-art models remarkably. Our code is publicly available at: https://github.com/thograce/C2FNet.
Learning Interpretable Concept Groups in CNNs
Authors: Varshneya Saurabh , Ledent Antoine , Vandermeulen Robert A. , Lei Yunwen , Enders Matthias , Borth Damian , Kloft Marius
Published: August 2021

We propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer into \emph{concept groups}, each of which is trained to learn a single visual concept. We achieve this through a novel regularization strategy that forces filters in the same group to be active in similar image regions for a given layer. We additionally use a regularizer to encourage a sparse weighting of the concept groups in each layer so that a few concept groups can have greater importance than others. We quantitatively evaluate CGL's model interpretability using standard interpretability evaluation techniques and find that our method increases interpretability scores in most cases. Qualitatively we compare the image regions which are most active under filters learned using CGL versus filters learned without CGL and find that CGL activation regions more strongly concentrate around semantically relevant features.
Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences
Authors: Wang Jiapeng , Wang Tianwei , Tang Guozhi , Jin Lianwen , Ma Weihong , Ding Kai , Huang Yichao
Published: August 2021

Visual information extraction (VIE) has attracted increasing attention in recent years. The existing methods usually first organized optical character recognition (OCR) results in plain texts and then utilized token-level category annotations as supervision to train a sequence tagging model. However, it expends great annotation costs and may be exposed to label confusion, the OCR errors will also significantly affect the final performance. In this paper, we propose a unified weakly-supervised learning framework called TCPNet (Tag, Copy or Predict Network), which introduces 1) an efficient encoder to simultaneously model the semantic and layout information in 2D OCR results, 2) a weakly-supervised training method that utilizes only sequence-level supervision; and 3) a flexible and switchable decoder which contains two inference modes: one (Copy or Predict Mode) is to output key information sequences of different categories by copying a token from the input or predicting one in each time step, and the other (Tag Mode) is to directly tag the input sequence in a single forward pass. Our method shows new state-of-the-art performance on several public benchmarks, which fully proves its effectiveness.
Dig into Multi-modal Cues for Video Retrieval with Hierarchical Alignment
Authors: Wang Wenzhe , Zhang Mengdan , Chen Runnan , Cai Guanyu , Zhou Penghao , Peng Pai , Guo Xiaowei , Wu Jian , Sun Xing
Published: August 2021

Multi-modal cues presented in videos are usually beneficial for the challenging video-text retrieval task on internet-scale datasets. Recent video retrieval methods take advantage of multi-modal cues by aggregating them to holistic high-level semantics for matching with text representations in a global view. In contrast to this global alignment, the local alignment of detailed semantics encoded within both multi-modal cues and distinct phrases is still not well conducted. Thus, in this paper, we leverage the hierarchical video-text alignment to fully explore the detailed diverse characteristics in multi-modal cues for fine-grained alignment with local semantics from phrases, as well as to capture a high-level semantic correspondence. Specifically, multi-step attention is learned for progressively comprehensive local alignment and a holistic transformer is utilized to summarize multi-modal cues for global alignment. With hierarchical alignment, our model outperforms state-of-the-art methods on three public video retrieval datasets.
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Authors: Wang Zhipeng , Wang Hao , Yan Jiexi , Wu Aming , Deng Cheng
Published: August 2021

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. Furthermore, a category-specific memory bank with sketch features is explored to reduce intra-class diversity in the sketch domain. Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TU-Berlin datasets.
Learning in Markets: Greed Leads to Chaos but Following the Price is Right
Authors: Cheung Yun Kuen , Leonardos Stefanos , Piliouras Georgios
Published: August 2021

We study learning dynamics in distributed production economies such as blockchain mining, peer-to-peer file sharing and crowdsourcing. These economies can be modelled as multi-product Cournot competitions or all-pay auctions (Tullock contests) when individual firms have market power, or as Fisher markets with quasi-linear utilities when every firm has negligible influence on market outcomes. In the former case, we provide a formal proof that Gradient Ascent (GA) can be Li-Yorke chaotic for a step size as small as Θ(1/n), where n is the number of firms. In stark contrast, for the Fisher market case, we derive a Proportional Response (PR) protocol that converges to market equilibrium. The positive results on the convergence of the PR dynamics are obtained in full generality, in the sense that they hold for Fisher markets with any quasi-linear utility functions. Conversely, the chaos results for the GA dynamics are established even in the simplest possible setting of two firms and one good, and they hold for a wide range of price functions with different demand elasticities. Our findings suggest that by considering multi-agent interactions from a market rather than a game-theoretic perspective, we can formally derive natural learning protocols which are stable and converge to effective outcomes rather than being chaotic.
Tracklet Proposal Network for Multi-Object Tracking on Point Clouds
Authors: Wu Hai , Li Qing , Wen Chenglu , Li Xin , Fan Xiaoliang , Wang Cheng
Published: August 2021

This paper proposes the first tracklet proposal network, named PC-TCNN, for Multi-Object Tracking (MOT) on point clouds. Our pipeline first generates tracklet proposals, then refines these tracklets and associates them to generate long trajectories. Specifically, object proposal generation and motion regression are first performed on a point cloud sequence to generate tracklet candidates. Then, spatial-temporal features of each tracklet are exploited and their consistency is used to refine the tracklet proposal. Finally, the refined tracklets across multiple frames are associated to perform MOT on the point cloud sequence. The PC-TCNN significantly improves the MOT performance by introducing the tracklet proposal design. On the KITTI tracking benchmark, it attains an MOTA of 91.75%, outperforming all submitted results on the online leaderboard.
GM-MLIC: Graph Matching based Multi-Label Image Classification
Authors: Wu Yanan , Liu He , Feng Songhe , Jin Yi , Lyu Gengyu , Wu Zizhang
Published: August 2021

Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and reformulate the task of MLIC as a instance-label matching selection problem. To model such problem, we propose a novel deep learning framework named Graph Matching based Multi-Label Image Classification (GM-MLIC), where Graph Matching (GM) scheme is introduced owing to its excellent capability of excavating the instance and label relationship. Specifically, we first construct an instance spatial graph and a label semantic graph respectively, and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.
Adversarial Feature Disentanglement for Long-Term Person Re-identification
Authors: Xu Wanlu , Liu Hong , Shi Wei , Miao Ziling , Lu Zhisheng , Chen Feihu
Published: August 2021

Most existing person re-identification methods are effective in short-term scenarios because of their appearance dependencies. However, these methods may fail in long-term scenarios where people might change their clothes. To this end, we propose an adversarial feature disentanglement network (AFD-Net) which contains intra-class reconstruction and inter-class adversary to disentangle the identity-related and identity-unrelated (clothing) features. For intra-class reconstruction, the person images with the same identity are represented and disentangled into identity and clothing features by two separate encoders, and further reconstructed into original images to reduce intra-class feature variations. For inter-class adversary, the disentangled features across different identities are exchanged and recombined to generate adversarial clothes-changing images for training, which makes the identity and clothing features more independent. Especially, to supervise these new generated clothes-changing images, a re-feeding strategy is designed to re-disentangle and reconstruct these new images for image-level self-supervision in the original image space and feature-level soft-supervision in the disentangled feature space. Moreover, we collect a challenging Market-Clothes dataset and a real-world PKU-Market-Reid dataset for evaluation. The results on one large-scale short-term dataset (Market-1501) and five long-term datasets (three public and two we proposed) confirm the superiority of our method against other state-of-the-art methods.
RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection
Authors: Yang Dongming , Zou Yuexian , Zhang Can , Cao Meng , Chen Jie
Published: August 2021

Human-Object Interaction (HOI) detection devotes to learn how humans interact with surrounding objects. Latest end-to-end HOI detectors are short of relation reasoning, which leads to inability to learn HOI-specific interactive semantics for predictions. In this paper, we therefore propose novel relation reasoning for HOI detection. We first present a progressive Relation-aware Frame, which brings a new structure and parameter sharing pattern for interaction inference. Upon the frame, an Interaction Intensifier Module and a Correlation Parsing Module are carefully designed, where: a) interactive semantics from humans can be exploited and passed to objects to intensify interactions, b) interactive correlations among humans, objects and interactions are integrated to promote predictions. Based on modules above, we construct an end-to-end trainable framework named Relation Reasoning Network (abbr. RR-Net). Extensive experiments show that our proposed RR-Net sets a new state-of-the-art on both V-COCO and HICO-DET benchmarks and improves the baseline about 5.5% and 9.8% relatively, validating that this first effort in exploring relation reasoning and integrating interactive semantics has brought obvious improvement for end-to-end HOI detection.
Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction
Authors: Yang Ruijing , Guan Ziyu , Yu Zitong , Feng Xiaoyi , Peng Jinye , Zhao Guoying
Published: August 2021

Automatic pain recognition is paramount for medical diagnosis and treatment. The existing works fall into three categories: assessing facial appearance changes, exploiting physiological cues, or fusing them in a multi-modal manner. However, (1) appearance changes are easily affected by subjective factors which impedes objective pain recognition. Besides, the appearance-based approaches ignore long-range spatial-temporal dependencies that are important for modeling expressions over time; (2) the physiological cues are obtained by attaching sensors on human body, which is inconvenient and uncomfortable. In this paper, we present a novel multi-task learning framework which encodes both appearance changes and physiological cues in a non-contact manner for pain recognition. The framework is able to capture both local and long-range dependencies via the proposed attention mechanism for the learned appearance representations, which are further enriched by temporally attended physiological cues (remote photoplethysmography, rPPG) that are recovered from videos in the auxiliary task. This framework is dubbed rPPG-enriched Spatio-Temporal Attention Network (rSTAN) and allows us to establish the state-of-the-art performance of non-contact pain recognition on publicly available pain databases. It demonstrates that rPPG predictions can be used as an auxiliary task to facilitate non-contact automatic pain recognition.
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
Authors: Yin Bangjie , Wang Wenxuan , Yao Taiping , Guo Junfeng , Kong Zelun , Ding Shouhong , Li Jilin , Liu Cong
Published: August 2021

Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples. However, existing adversarial examples against face recognition systems either lack transferability to black-box models, or fail to be implemented in practice. In this paper, we propose a unified adversarial face generation method - Adv-Makeup, which can realize imperceptible and transferable attack under the black-box setting. Adv-Makeup develops a task-driven makeup generation method with the blending module to synthesize imperceptible eye shadow over the orbital region on faces. And to achieve transferability, Adv-Makeup implements a fine-grained meta-learning based adversarial attack strategy to learn more vulnerable or sensitive features from various models. Compared to existing techniques, sufficient visualization results demonstrate that Adv-Makeup is capable to generate much more imperceptible attacks under both digital and physical scenarios. Meanwhile, extensive quantitative experiments show that Adv-Makeup can significantly improve the attack success rate under black-box setting, even attacking commercial systems.
Multimodal Transformer Networks for Pedestrian Trajectory Prediction
Authors: Yin Ziyi , Liu Ruijin , Xiong Zhiliang , Yuan Zejian
Published: August 2021

We consider the problem of forecasting the future locations of pedestrians in an ego-centric view of a moving vehicle. Current CNNs or RNNs are flawed in capturing the high dynamics of motion between pedestrians and the ego-vehicle, and suffer from the massive parameter usages due to the inefficiency of learning long-term temporal dependencies. To address these issues, we propose an efficient multimodal transformer network that aggregates the trajectory and ego-vehicle speed variations at a coarse granularity and interacts with the optical flow in a fine-grained level to fill the vacancy of highly dynamic motion. Specifically, a coarse-grained fusion stage fuses the information between trajectory and ego-vehicle speed modalities to capture the general temporal consistency. Meanwhile, a fine-grained fusion stage merges the optical flow in the center area and pedestrian area, which compensates the highly dynamic motion of ego-vehicle and target pedestrian. Besides, the whole network is only attention-based that can efficiently model long-term sequences for better capturing the temporal variations. Our multimodal transformer is validated on the PIE and JAAD datasets and achieves state-of-the-art performance with the most light-weight model size. The codes are available at https://github.com/ericyinyzy/MTN_trajectory.
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
Authors: Yu Jing , Chai Yuan , Wang Yujing , Hu Yue , Wu Qi
Published: August 2021

Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships. The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models. The code is available at: https://github.com/CYVincent/Scene-Graph-Transformer-CogTree.
Dual-Cross Central Difference Network for Face Anti-Spoofing
Authors: Yu Zitong , Qin Yunxiao , Zhao Hengshuang , Li Xiaobai , Zhao Guoying
Published: August 2021

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Recently, central difference convolution (CDC) has shown its excellent representation capacity for the FAS task via leveraging local gradient features. However, aggregating central difference clues from all neighbors/directions simultaneously makes the CDC redundant and sub-optimized in the training phase. In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively. It is interesting to find that, with only five ninth parameters and less computational cost, C-CDC even outperforms the full directional CDC. Based on these two decoupled C-CDC, a powerful Dual-Cross Central Difference Network (DC-CDN) is established with Cross Feature Interaction Modules (CFIM) for mutual relation mining and local detailed representation enhancement. Furthermore, a novel Patch Exchange (PE) augmentation strategy for FAS is proposed via simply exchanging the face patches as well as their dense labels from random samples. Thus, the augmented samples contain richer live/spoof patterns and diverse domain distributions, which benefits the intrinsic and robust feature learning. Comprehensive experiments are performed on four benchmark datasets with three testing protocols to demonstrate our state-of-the-art performance.
Detecting Deepfake Videos with Temporal Dropout 3DCNN
Authors: Zhang Daichi , Li Chenyu , Lin Fanzhao , Zeng Dan , Ge Shiming
Published: August 2021

While the abuse of deepfake technology has brought about a serious impact on human society, the detection of deepfake videos is still very challenging due to their highly photorealistic synthesis on each frame. To address that, this paper aims to leverage the possible inconsistent cues among video frames and proposes a Temporal Dropout 3-Dimensional Convolutional Neural Network (TD-3DCNN) to detect deepfake videos. In the approach, the fixed-length frame volumes sampled from a video are fed into a 3-Dimensional Convolutional Neural Network (3DCNN) to extract features across different scales and identified whether they are real or fake. Especially, a temporal dropout operation is introduced to randomly sample frames in each batch. It serves as a simple yet effective data augmentation and can enhance the representation and generalization ability, avoiding model overfitting and improving detecting accuracy. In this way, the resulting video-level classifier is accurate and effective to identify deepfake videos. Extensive experiments on benchmarks including Celeb-DF(v2) and DFDC clearly demonstrate the effectiveness and generalization capacity of our approach.
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification
Authors: Zhang Guoqing , Chen Yuhao , Lin Weisi , Chandran Arun , Jing Xuan
Published: August 2021

As a prevailing task in video surveillance and forensics field, person re-identification (re-ID) aims to match person images captured from non-overlapped cameras. In unconstrained scenarios, person images often suffer from the resolution mismatch problem, i.e., Cross-Resolution Person Re-ID. To overcome this problem, most existing methods restore low resolution (LR) images to high resolution (HR) by super-resolution (SR). However, they only focus on the HR feature extraction and ignore the valid information from original LR images. In this work, we explore the influence of resolutions on feature extraction and develop a novel method for cross-resolution person re-ID called Multi-Resolution Representations Joint Learning (MRJL). Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN). The RRN uses an input image to construct a HR version and a LR version with an encoder and two decoders, while the DFFN adopts a dual-branch structure to generate person representations from multi-resolution images. Comprehensive experiments on five benchmarks verify the superiority of the proposed MRJL over the relevent state-of-the-art methods.
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
Authors: Zhao Weibing , Yan Xu , Gao Jiantao , Zhang Ruimao , Zhang Jiayan , Li Zhen , Wu Song , Cui Shuguang
Published: August 2021

Point Cloud Sampling and Recovery (PCSR) is critical for massive real-time point cloud collection and processing since raw data usually requires large storage and computation. This paper addresses a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarded points in a case-agnostic manner (i.e., without additional storage for point relationships)? We propose a novel Locally Invertible Embedding (PointLIE) framework to unify the point cloud sampling and upsampling into one single framework through bi-directional learning. Specifically, PointLIE decouples the local geometric relationships between discarded points from the sampled points by progressively encoding the neighboring offsets to a latent variable. Once the latent variable is forced to obey a pre-defined distribution in the forward sampling path, the recovery can be achieved effectively through inverse operations. Taking the recover-pleasing sampled points and a latent embedding randomly drawn from the specified distribution as inputs, PointLIE can theoretically guarantee the fidelity of reconstruction and outperform state-of-the-arts quantitatively and qualitatively.
PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation
Authors: Zhu Yiran , Xu Xing , Shen Fumin , Ji Yanli , Gao Lianli , Shen Heng Tao
Published: August 2021

Graph neural networks (GNNs) have been widely used in the 3D human pose estimation task, since the pose representation of a human body can be naturally modeled by the graph structure. Generally, most of the existing GNN-based models utilize the restricted receptive ﬁelds of ﬁlters and single-scale information, while neglecting the valuable multi-scale contextual information. To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. In our proposed PoseGTAC model, Graph Atrous Convolution (GAC) and Graph Transformer Layer (GTL), respectively for the extraction of local multi-scale and global long-range information, are combined and stacked in an encoder-decoder structure, where graph pooling and unpooling are adopted for the interaction of multi-scale information from local to global (e.g., part-scale and body-scale). Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that the proposed PoseGTAC model exceeds all previous methods and achieves state-of-the-art performance.
Multi-Agent Intention Progression with Black-Box Agents
Authors: Dann Michael , Yao Yuan , Logan Brian , Thangarajah John
Published: August 2021

We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. That is, while their goals are known, the precise programs used to achieve these goals are not known. In our approach, agents use an abstraction of their own program called a partially-ordered goal-plan tree (pGPT) to schedule their intentions and predict the actions of other agents. We show how a pGPT can be derived from the program of a BDI agent, and present an approach based on Monte Carlo Tree Search (MCTS) for scheduling an agent's intentions using pGPTs. We evaluate our pGPT-based approach in cooperative, selfish and adversarial multi-agent settings, and show that it out-performs MCTS-based scheduling where agents assume that other agents have the same program as themselves.
Solving Graph Homomorphism and Subgraph Isomorphism Problems Faster Through Clique Neighbourhood Constraints
Authors: Kraiczy Sonja , McCreesh Ciaran
Published: August 2021

Graph homomorphism problems involve finding adjacency-preserving mappings between two given graphs. Although theoretically hard, these problems can often be solved in practice using constraint programming algorithms. We show how techniques from the state-of-the-art in subgraph isomorphism solving can be applied to broader graph homomorphism problems, and introduce a new form of filtering based upon clique-finding. We demonstrate empirically that this filtering is effective for the locally injective graph homomorphism and subgraph isomorphism problems, and gives the first practical constraint programming approach to finding general graph homomorphisms.
Learning Implicitly with Noisy Data in Linear Arithmetic
Authors: Rader Alexander , Mocanu Ionela G , Belle Vaishak , Juba Brendan
Published: August 2021

Robust learning in expressive languages with real-world data continues to be a challenging task. Numerous conventional methods appeal to heuristics without any assurances of robustness. While probably approximately correct (PAC) Semantics offers strong guarantees, learning explicit representations is not tractable, even in propositional logic. However, recent work on so-called “implicit" learning has shown tremendous promise in terms of obtaining polynomial-time results for fragments of first-order logic. In this work, we extend implicit learning in PAC-Semantics to handle noisy data in the form of intervals and threshold uncertainty in the language of linear arithmetic. We prove that our extended framework keeps the existing polynomial-time complexity guarantees. Furthermore, we provide the first empirical investigation of this hitherto purely theoretical framework. Using benchmark problems, we show that our implicit approach to learning optimal linear programming objective constraints significantly outperforms an explicit approach in practice.
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation
Authors: Chen Gaode , Zhang Xinghua , Zhao Yanyan , Xue Cong , Xiang Ji
Published: August 2021

Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overall representation based on the user’s behavior sequence, which can not sufficiently reflect the multiple interests of the user. To this end, we propose a novel method called PIMI to mitigate this issue. PIMI can model the user’s multi-interest representation effectively by considering both the periodicity and interactivity in the item sequence. Specifically, we design a periodicity-aware module to utilize the time interval information between user’s behaviors. Meanwhile, an ingenious graph is proposed to enhance the interactivity between items in user’s behavior sequence, which can capture both global and local item features. Finally, a multi-interest extraction module is applied to describe user’s multiple interests based on the obtained item representation. Extensive experiments on two real-world datasets Amazon and Taobao show that PIMI outperforms state-of-the-art methods consistently.