pwc-archive/papers-with-abstracts huggingface dataset:
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection
Authors: Yongri Piao,  Zhengkun Rong,  Miao Zhang,  Weisong Ren,  Huchuan Lu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data. This inevitably incurs extra computational costs and memory consumption, and using depth data during testing may hinder the practical applications of RGB-D saliency detection. To tackle these two dilemmas, we propose a depth distiller (A2dele) to explore the way of using network prediction and attention as two bridges to transfer the depth knowledge from the depth stream to the RGB stream. First, by adaptively minimizing the differences between predictions generated from the depth stream and RGB stream, we realize the desired control of pixel-wise depth knowledge transferred to the RGB stream. Second, to transfer the localization knowledge to RGB features, we encourage consistencies between the dilated prediction of the depth stream and the attention map from the RGB stream. As a result, we achieve a lightweight architecture without use of depth data at test time by embedding our A2dele. Our extensive experimental evaluation on five benchmarks demonstrate that our RGB stream achieves state-of-the-art performance, which tremendously minimizes the model size by 76% and runs 12 times faster, compared with the best performing method. Furthermore, our A2dele can be applied to existing RGB-D networks to significantly improve their efficiency while maintaining performance (boosts FPS by nearly twice for DMRA and 3 times for CPFP).
Deep Fair Clustering for Visual Learning
Authors: Peizhao Li,  Han Zhao,  Hongfu Liu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Clustering, Deep Clustering, Fairness

Fair clustering aims to hide sensitive attributes during data partition by balancing the distribution of protected subgroups in each cluster. Existing work attempts to address this problem by reducing it to a classical balanced clustering with a constraint on the proportion of protected subgroups of the input space. However, the input space may limit the clustering performance, and so far only low-dimensional datasets have been considered. In light of these limitations, in this paper, we propose Deep Fair Clustering (DFC) to learn fair and clustering-favorable representations for clustering simultaneously. Our approach could effectively filter out sensitive attributes from representations, and also lead to representations that are amenable for the following cluster analysis. Theoretically, we show that our fairness constraint in DFC will not incur much loss in terms of several clustering metrics. Empirically, we provide extensive experimental demonstrations on four visual datasets to corroborate the superior performance of the proposed approach over existing fair clustering and deep clustering methods on both cluster validity and fairness criterion.
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss
Authors: Xiang Gu,  Jian Sun,  Zongben Xu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Domain Adaptation, Pseudo Label

Adversarial domain adaptation (DA) has been an effective approach for learning domain-invariant features by adversarial training. In this paper, we propose a novel adversarial DA approach completely defined in spherical feature space, in which we define spherical classifier for label prediction and spherical domain discriminator for discriminating domain labels. To utilize pseudo-label robustly, we develop a robust pseudo-label loss in the spherical feature space, which weights the importance of estimated labels of target data by posterior probability of correct labeling, modeled by Gaussian-uniform mixture model in spherical feature space. Extensive experiments show that our method achieves state-of-the-art results, and also confirm effectiveness of spherical classifier, spherical discriminator and spherical robust pseudo-label loss.
Stochastic Classifiers for Unsupervised Domain Adaptation
Authors: Zhihe Lu,  Yongxin Yang,  Xiatian Zhu,  Cong Liu,  Yi-Zhe Song,  Tao Xiang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Domain Adaptation, image-classification, Image Classification, Semantic Segmentation, Unsupervised Domain Adaptation

A common strategy adopted by existing state-of-the-art unsupervised domain adaptation (UDA) methods is to employ two classifiers to identify the misaligned local regions between source and target domain. Following the 'wisdom of the crowd' principle, one has to ask: why stop at two? Indeed, we find that using more classifiers leads to better performance, but also introduces more model parameters, therefore risking overfitting. In this paper, we introduce a novel method called STochastic clAssifieRs (STAR) for addressing this problem. Instead of representing one classifier as a weight vector, STAR models it as a Gaussian distribution with its variance representing the inter-classifier discrepancy. With STAR, we can now sample an arbitrary number of classifiers from the distribution, whilst keeping the model size the same as having two classifiers. Extensive experiments demonstrate that a variety of existing UDA methods can greatly benefit from STAR and achieve the state-of-the-art performance on both image classification and semantic segmentation tasks.
Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection
Authors: Huajun Zhou,  Xiaohua Xie,  Jian-Huang Lai,  Zixuan Chen,  Lingxiao Yang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Decoder, Saliency Detection, Vocal Bursts Valence Prediction

Recently, contour information largely improves the performance of saliency detection. However, the discussion on the correlation between saliency and contour remains scarce. In this paper, we first analyze such correlation and then propose an interactive two-stream decoder to explore multiple cues, including saliency, contour and their correlation. Specifically, our decoder consists of two branches, a saliency branch and a contour branch. Each branch is assigned to learn distinctive features for predicting the corresponding map. Meanwhile, the intermediate connections are forced to learn the correlation by interactively transmitting the features from each branch to the other one. In addition, we develop an adaptive contour loss to automatically discriminate hard examples during learning process. Extensive experiments on six benchmarks well demonstrate that our network achieves competitive performance with a fast speed around 50 FPS. Moreover, our VGG-based model only contains 17.08 million parameters, which is significantly smaller than other VGG-based approaches. Code has been made available at: https://github.com/moothes/ITSD-pytorch.
Data-Efficient Semi-Supervised Learning by Reliable Edge Mining
Authors: Peibin Chen,  Tao Ma,  Xu Qin,  Weidi Xu,  Shuchang Zhou
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

Learning powerful discriminative features is a challenging task in Semi-Supervised Learning, as the estimation of the feature space is more likely to be wrong with scarcer labeled data. Previous methods utilize a relation graph with edges representing 'similarity' or 'dissimilarity' between nodes. Similar nodes are forced to output consistent features, while dissimilar nodes are forced to be inconsistent. However, since unlabeled data may be wrongly labeled, the judgment of edges may be unreliable. Besides, the nodes connected by edges may already be well fitted, thus contributing little to the model training. We propose Reliable Edge Mining (REM), which forms a reliable graph by only selecting reliable and useful edges. Guided by the graph, the feature extractor is able to learn discriminative features in a data-efficient way, and consequently boosts the accuracy of the learned classifier. Visual analyses show that the features learned are more discriminative and better reveals the underlying structure of the data. REM can be combined with perturbation-based methods like Pi-model, TempEns and Mean Teacher to further improve accuracy. Experiments prove that our method is data-efficient on simple tasks like SVHN and CIFAR-10, and achieves state-of-the-art results on the challenging CIFAR-100.
Progressive Adversarial Networks for Fine-Grained Domain Adaptation
Authors: Sinan Wang,  Xinyang Chen,  Yunbo Wang,  Mingsheng Long,  Jianmin Wang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Domain Adaptation, Fine-Grained Visual Categorization

Fine-grained visual categorization has long been considered as an important problem, however, its real application is still restricted, since precisely annotating a large fine-grained image dataset is a laborious task and requires expert-level human knowledge. A solution to this problem is applying domain adaptation approaches to fine-grained scenarios, where the key idea is to discover the commonality between existing fine-grained image datasets and massive unlabeled data in the wild. The main technical bottleneck lies in that the large inter-domain variation will deteriorate the subtle boundaries of small inter-class variation during domain alignment. This paper presents the Progressive Adversarial Networks (PAN) to align fine-grained categories across domains with a curriculum-based adversarial learning framework. In particular, throughout the learning process, domain adaptation is carried out through all multi-grained features, progressively exploiting the label hierarchy from coarse to fine. The progressive learning is applied upon both category classification and domain alignment, boosting both the discriminability and the transferability of the fine-grained features. Our method is evaluated on three benchmarks, two of which are proposed by us, and it outperforms the state-of-the-art domain adaptation methods.
Hyperbolic Visual Embedding Learning for Zero-Shot Recognition
Authors: Shaoteng Liu,  Jingjing Chen,  Liangming Pan,  Chong-Wah Ngo,  Tat-Seng Chua,  Yu-Gang Jiang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Zero-Shot Learning

This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zero-shot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but with five times lower embedding dimensions. Our code is publicly available (https://github.com/ShaoTengLiu/Hyperbolic_ZSL).
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning
Authors: Mei Wang,  Weihong Deng
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Face Recognition, Fairness, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians. To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses. A reinforcement learning based race balance network (RL-RBN) is proposed. We formulate the process of finding the optimal margins for non-Caucasians as a Markov decision process and employ deep Q-learning to learn policies for an agent to select appropriate margin by approximating the Q-value function. Guided by the agent, the skewness of feature scatter between races can be reduced. Besides, we provide two ethnicity aware training datasets, called BUPT-Globalface and BUPT-Balancedface dataset, which can be utilized to study racial bias from both data and algorithm aspects. Extensive experiments on RFW database show that RL-RBN successfully mitigates racial bias and learns more balanced performance.
DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data
Authors: Vignesh Ramanathan,  Rui Wang,  Dhruv Mahajan
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

Large detection datasets have a long tail of lowshot classes with very few bounding box annotations. We wish to improve detection for lowshot classes with weakly labelled web-scale datasets only having image-level labels. This requires a detection framework that can be jointly trained with limited number of bounding box annotated images and large number of weakly labelled images. Towards this end, we propose a modification to the FRCNN model to automatically infer label assignment for objects proposals from weakly labelled images during training. We pose this label assignment as a Linear Program with constraints on the number and overlap of object instances in an image. We show that this can be solved efficiently during training for weakly labelled images. Compared to just training with few annotated examples, augmenting with weakly labelled examples in our framework provides significant gains. We demonstrate this on the LVIS dataset 3.5 gain in AP as well as different lowshot variants of the COCO dataset. We provide a thorough analysis of the effect of amount of weakly labelled and fully labelled data required to train the detection model. Our DLWL framework can also outperform self-supervised baselines like omni-supervision for lowshot classes.
Unsupervised Deep Shape Descriptor With Point Distribution Learning
Authors: Yi Shi,  Mengchen Xu,  Shuaihang Yuan,  Yi Fang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Shape Classification, 3D Shape Retrieval, General Classification, Retrieval

Deep learning models have achieved great success in supervised shape descriptor learning for 3D shape retrieval, classification, and correspondence. However, the unsupervised shape descriptor calculated via deep learning is less studied than that of supervised ones due to the design challenges of unsupervised neural network architecture. This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning. In our approach, we firstly associate each point with a Gaussian, and the point clouds are modeled as the distribution of the points. We then use deep neural networks (DNNs) to model a maximum likelihood estimation process that is traditionally solved with an iterative Expectation-Maximization (EM) process. Our key novelty is that "training" these DNNs with unsupervised self-correspondence L2 distance loss will elegantly reveal the statically significant deep shape descriptor representation for the distribution of the point clouds. We have conducted experiments over various 3D datasets. Qualitative and quantitative comparisons demonstrate that our proposed method achieves superior classification performance over existing unsupervised 3D shape descriptors. In addition, we verified the following attractive properties of our shape descriptor through experiments: multi-scale shape representation, robustness to shape rotation, and robustness to noise.
Stylization-Based Architecture for Fast Deep Exemplar Colorization
Authors: Zhongyou Xu,  Tingting Wang,  Faming Fang,  Yun Sheng,  Guixu Zhang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Colorization, Image Stylization

Exemplar-based colorization aims to add colors to a grayscale image guided by a content related reference im- age. Existing methods are either sensitive to the selection of reference images (content, position) or extremely time and resource consuming, which limits their practical applica- tion. To tackle these problems, we propose a deep exemplar colorization architecture inspired by the characteristics of stylization in feature extracting and blending. Our coarse- to-fine architecture consists of two parts: a fast transfer sub-net and a robust colorization sub-net. The transfer sub- net obtains a coarse chrominance map via matching basic feature statistics of the input pairs in a progressive way. The colorization sub-net refines the map to generate the final re- sults. The proposed end-to-end network can jointly learn faithful colorization with a related reference and plausible color prediction with unrelated reference. Extensive exper- imental validation demonstrates that our approach outper- forms the state-of-the-art methods in less time whether in exemplar-based colorization or image stylization tasks.
Interactive Multi-Label CNN Learning With Partial Labels
Authors: Dat Huynh,  Ehsan Elhamifar
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

We address the problem of efficient end-to-end learning a multi-label Convolutional Neural Network (CNN) on training images with partial labels. Training a CNN with partial labels, hence a small number of images for every label, using the standard cross-entropy loss is prone to overfitting and performance drop. We introduce a new loss function that regularizes the cross-entropy loss with a cost function that measures the smoothness of labels and features of images on the data manifold. Given that optimizing the new loss function over the CNN parameters requires learning similarities among labels and images, which itself depends on knowing the parameters of the CNN, we develop an efficient interactive learning framework in which the two steps of similarity learning and CNN training interact and improve the performance of each another. Our method learns the CNN parameters without requiring keeping all training data in the memory, allows to learn few informative similarities only for images in each mini-batch and handles changing feature representations. By extensive experiments on Open Images, CUB and MS-COCO datasets,we demonstrate the effectiveness of our method. In particular, on the large-scale Open Images dataset, we improve the state of the art by 1.02% in mAP score over 5,000 classes.
Minimizing Discrete Total Curvature for Image Processing
Authors: Qiuxiang Zhong,  Yutong Li,  Yijie Yang,  Yuping Duan
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

The curvature regularities have received growing attention with the advantage of providing strong priors in the continuity of edges in image processing applications. However, owing to the non-convex and non-smooth properties of the high-order regularizer, the numerical solution becomes challenging in real-time tasks. In this paper, we propose a novel curvature regularity, the total curvature (TC), by minimizing the normal curvatures along different directions. We estimate the normal curvatures discretely in the local neighborhood according to differential geometry theory. The resulting curvature regularity can be regarded as a re-weighted total variation (TV) minimization problem, which can be efficiently solved by the alternating direction method of multipliers (ADMM) based algorithm. By comparing with TV and Euler's elastica energy, we demonstrate the effectiveness and superiority of the total curvature regularity for various image processing applications.
Discovering Synchronized Subsets of Sequences: A Large Scale Solution
Authors: Evangelos Sariyanidi,  Casey J. Zampella,  Keith G. Bartley,  John D. Herrington,  Theodore D. Satterthwaite,  Robert T. Schultz,  Birkan Tunc
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Time Series, Time Series Analysis

Finding the largest subset of sequences (i.e., time series) that are correlated above a certain threshold, within large datasets, is of significant interest for computer vision and pattern recognition problems across domains, including behavior analysis, computational biology, neuroscience, and finance. Maximal clique algorithms can be used to solve this problem, but they are not scalable. We present an approximate, but highly efficient and scalable, method that represents the search space as a union of sets called epsilon-expanded clusters, one of which is theoretically guaranteed to contain the largest subset of synchronized sequences. The method finds synchronized sets by fitting a Euclidean ball on epsilon-expanded clusters, using Jung's theorem. We validate the method on data from the three distinct domains of facial behavior analysis, finance, and neuroscience, where we respectively discover the synchrony among pixels of face videos, stock market item prices, and dynamic brain connectivity data. Experiments show that our method produces results comparable to, but up to 300 times faster than, maximal clique algorithms, with speed gains increasing exponentially with the number of input sequences.
Attack to Explain Deep Representation
Authors: Mohammad A. A. K. Jalwana,  Naveed Akhtar,  Mohammed Bennamoun,  Ajmal Mian
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Generation, Image Manipulation

Deep visual models are susceptible to extremely low magnitude perturbations to input images. Though carefully crafted, the perturbation patterns generally appear noisy, yet they are able to perform controlled manipulation of model predictions. This observation is used to argue that deep representation is misaligned with human perception. This paper counter-argues and proposes the first attack on deep learning that aims at explaining the learned representation instead of fooling it. By extending the input domain of the manipulative signal and employing a model faithful channelling, we iteratively accumulate adversarial perturbations for a deep model. The accumulated signal gradually manifests itself as a collection of visually salient features of the target label (in model fooling), casting adversarial perturbations as primitive features of the target label. Our attack provides the first demonstration of systematically computing perturbations for adversarially non-robust classifiers that comprise salient visual features of objects. We leverage the model explaining character of our algorithm to perform image generation, inpainting and interactive image manipulation by attacking adversarially robust classifiers. The visually appealing results across these applications demonstrate the utility of our attack (and perturbations in general) beyond model fooling.
Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints
Authors: Michael Strecke,  Jorg Stuckler
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Object, Scene Understanding

Dynamic scene understanding is an essential capability in robotics and VR/AR. In this paper we propose Co-Section, an optimization-based approach to 3D dynamic scene reconstruction, which infers hidden shape information from intersection constraints. An object-level dynamic SLAM frontend detects, segments, tracks and maps dynamic objects in the scene. Our optimization backend completes the shapes using hull and intersection constraints between the objects. In experiments, we demonstrate our approach on real and synthetic dynamic scene datasets. We also assess the shape completion performance of our method quantitatively. To the best of our knowledge, our approach is the first method to incorporate such physical plausibility constraints on object intersections for shape completion of dynamic objects in an energy minimization framework.