pwc-archive/papers-with-abstracts huggingface dataset:
Learning Unseen Concepts via Hierarchical Decomposition and Composition
Authors: Muli Yang,  Cheng Deng,  Junchi Yan,  Xianglong Liu,  Dacheng Tao
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Diversity

Composing and recognizing new concepts from known sub-concepts has been a fundamental and challenging vision task, mainly due to 1) the diversity of sub-concepts and 2) the intricate contextuality between sub-concepts and their corresponding visual features. However, most of the current methods simply treat the contextuality as rigid semantic relationships and fail to capture fine-grained contextual correlations. We propose to learn unseen concepts in a hierarchical decomposition-and-composition manner. Considering the diversity of sub-concepts, our method decomposes each seen image into visual elements according to its labels, and learns corresponding sub-concepts in their individual subspaces. To model intricate contextuality between sub-concepts and their visual features, compositions are generated from these subspaces in three hierarchical forms, and the composed concepts are learned in a unified composition space. To further refine the captured contextual relationships, adaptively semi-positive concepts are defined and then learned with pseudo supervision exploited from the generated compositions. We validate the proposed approach on two challenging benchmarks, and demonstrate its superiority over state-of-the-art approaches.
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation
Authors: Tao Zhou,  Huazhu Fu,  Chen Gong,  Jianbing Shen,  Ling Shao,  Fatih Porikli
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Motion Segmentation

Human motion segmentation based on transfer subspace learning is a rising interest in action-related tasks. Although progress has been made, there are still several issues within the existing methods. First, existing methods transfer knowledge from source data to target tasks by learning domain-invariant features, but they ignore to preserve domain-specific knowledge. Second, the transfer subspace learning is employed in either low-level or high-level feature spaces, but few methods consider fusing multi-level features for subspace learning. To this end, we propose a novel multi-mutual consistency induced transfer subspace learning framework for human motion segmentation. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces and reduces the distribution gap between them through a multi-mutual consistency learning strategy. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Our model also conducts the transfer subspace learning on different layers to capture multi-level structural information. Further, to preserve the temporal correlations, we project the learned representations into a block-like space. The proposed model is efficiently optimized by using the Augmented Lagrange Multiplier (ALM) algorithm. Experimental results on four human motion datasets demonstrate the effectiveness of our method over other state-of-the-art approaches.
Learning Saliency Propagation for Semi-Supervised Instance Segmentation
Authors: Yanzhao Zhou,  Xin Wang,  Jianbin Jiao,  Trevor Darrell,  Fisher Yu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation, Semi-Supervised Instance Segmentation

Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. We aim to improve the accuracy of the existing instance segmentation models by utilizing a large amount of detection supervision. We propose ShapeProp, which learns to activate the salient regions within the object detection and propagate the areas to the whole instance through an iterative learnable message passing module. ShapeProp can benefit from more bounding box supervision to locate the instances more accurately and utilize the feature activations from the larger number of instances to achieve more accurate segmentation. We extensively evaluate ShapeProp on three datasets (MS COCO, PASCAL VOC, and BDD100k) with different supervision setups based on both two-stage (Mask R-CNN) and single-stage (RetinaMask) models. The results show our method establishes new states of the art for semi-supervised instance segmentation.
Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval
Authors: Kaiyue Pang,  Yongxin Yang,  Timothy M. Hospedales,  Tao Xiang,  Yi-Zhe Song
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Retrieval, Retrieval, Sketch-Based Image Retrieval

ImageNet pre-training has long been considered crucial by the fine-grained sketch-based image retrieval (FG-SBIR) community due to the lack of large sketch-photo paired datasets for FG-SBIR training. In this paper, we propose a self-supervised alternative for representation pre-training. Specifically, we consider the jigsaw puzzle game of recomposing images from shuffled parts. We identify two key facets of jigsaw task design that are required for effective FG-SBIR pre-training. The first is formulating the puzzle in a mixed-modality fashion. Second we show that framing the optimisation as permutation matrix inference via Sinkhorn iterations is more effective than the common classifier formulation of Jigsaw self-supervision. Experiments show that this self-supervised pre-training strategy significantly outperforms the standard ImageNet-based pipeline across all four product-level FG-SBIR benchmarks. Interestingly it also leads to improved cross-category generalisation across both pre-train/fine-tune and fine-tune/testing stages.
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks
Authors: Hang Zhou,  Dongdong Chen,  Jing Liao,  Kejiang Chen,  Xiaoyi Dong,  Kunlin Liu,  Weiming Zhang,  Gang Hua,  Nenghai Yu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6

Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samples produced from various attack methods, including optimization-based 3D Carlini-Wagner attack, gradient-based iterative fast gradient method, and skeleton-detach based point-dropping. However, after a careful analysis, these methods are either extremely slow because of the optimization/iterative scheme, or not flexible to support targeted attack of a specific category. To overcome these shortcomings, this paper proposes a novel label guided adversarial network (LG-GAN) for real-time flexible targeted point cloud attack. To the best of our knowledge, this is the first generation based 3D point cloud attack method. By feeding the original point clouds and target attack label into LG-GAN, it can learn how to deform the point clouds to mislead the recognition network into the specific label only with a single forward pass. In detail, LG-GAN first leverages one multi-branch adversarial network to extract hierarchical features of the input point clouds, then incorporates the specified label information into multiple intermediate features using the label encoder. Finally, the encoded features will be fed into the coordinate reconstruction decoder to generate the target adversarial sample. By evaluating different point-cloud recognition models (e.g., PointNet, PointNet++ and DGCNN), we demonstrate that the proposed LG-GAN can support flexible targeted attack on the fly while guaranteeing good attack performance and higher efficiency simultaneously.
Deepstrip: High-Resolution Boundary Refinement
Authors: Peng Zhou,  Brian Price,  Scott Cohen,  Gregg Wilensky,  Larry S. Davis
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Prediction, Vocal Bursts Intensity Prediction

In this paper, we target refining the boundaries in high resolution images given low resolution masks. For memory and computation efficiency, we propose to convert the regions of interest into strip images and compute a boundary prediction in the strip domain. To detect the target boundary, we present a framework with two prediction layers. First, all potential boundaries are predicted as an initial prediction and then a selection layer is used to pick the target boundary and smooth the result. To encourage accurate prediction, a loss which measures the boundary distance in strip domain is introduced. In addition, we enforce a matching consistency and C0 continuity regularization to the network to reduce false alarms. Extensive experiments on both public and a newly created high resolution dataset strongly validate our approach.
Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification
Authors: Guangcong Wang,  Jian-Huang Lai,  Wenqi Liang,  Guangrun Wang
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Person Re-Identification, Transfer Learning

Most of the existing person re-identification (re-ID) methods achieve promising accuracy in a supervised manner, but they assume the identity labels of the target domain is available. This greatly limits the scalability of person re-ID in real-world scenarios. Therefore, the current person re-ID community focuses on the cross-domain person re-ID that aims to transfer the knowledge from a labeled source domain to an unlabeled target domain and exploits the specific knowledge from the data distribution of the target domain to further improve the performance. To reduce the gap between the source and target domains, we propose a Smoothing Adversarial Domain Attack (SADA) approach that guides the source domain images to align the target domain images by using a trained camera classifier. To stabilize a memory trace of cross-domain knowledge transfer after its initial acquisition from the source domain, we propose a p-Memory Reconsolidation (pMR) method that reconsolidates the source knowledge with a small probability p during the self-training of the target domain. With both SADA and pMR, the proposed method significantly improves the cross-domain person re-ID. Extensive experiments on Market-1501 and DukeMTMC-reID benchmarks show that our pMR-SADA outperforms all of the state-of-the-arts by a large margin.
Density-Based Clustering for 3D Object Detection in Point Clouds
Authors: Syeda Mariam Ahmed,  Chee Meng Chew
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Object Detection, Clustering, Foreground Segmentation, Instance Segmentation, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

Current 3D detection networks either rely on 2D object proposals or try to directly predict bounding box parameters from each point in a scene. While former methods are dependent on performance of 2D detectors, latter approaches are challenging due to the sparsity and occlusion in point clouds, making it difficult to regress accurate parameters. In this work, we introduce a novel approach for 3D object detection that is significant in two main aspects: a) cascaded modular approach that focuses the receptive field of each module on specific points in the point cloud, for improved feature learning and b) a class agnostic instance segmentation module that is initiated using unsupervised clustering. The objective of a cascaded approach is to sequentially minimize the number of points running through the network. While three different modules perform the tasks of background-foreground segmentation, class agnostic instance segmentation and object detection, through individually trained point based networks. We also evaluate bayesian uncertainty in modules, demonstrating the over all level of confidence in our prediction results. Performance of the network is evaluated on the SUN RGB-D benchmark dataset, that demonstrates an improvement as compared to state-of-the-art methods.
Warp to the Future: Joint Forecasting of Features and Feature Motion
Authors: Josip Saric,  Marin Orsic,  Tonci Antunovic,  Sacha Vrazic,  Sinisa Segvic
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Semantic Segmentation

We address anticipation of scene development by forecasting semantic segmentation of future frames. Several previous works approach this problem by F2F (feature-to-feature) forecasting where future features are regressed from observed features. Different from previous work, we consider a novel F2M (feature-to-motion) formulation, which performs the forecast by warping observed features according to regressed feature flow. This formulation models a causal relationship between the past and the future, and regularizes inference by reducing dimensionality of the forecasting target. However, emergence of future scenery which was not visible in observed frames can not be explained by warping. We propose to address this issue by complementing F2M forecasting with the classic F2F approach. We realize this idea as a multi-head F2MF model built atop shared features. Experiments show that the F2M head prevails in static parts of the scene while the F2F head kicks-in to fill-in the novel regions. The proposed F2MF model operates in synergy with correlation features and outperforms all previous approaches both in short-term and mid-term forecast on the Cityscapes dataset.
Cross-Modal Cross-Domain Moment Alignment Network for Person Search
Authors: Ya Jing,  Wei Wang,  Liang Wang,  Tieniu Tan
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: cross-modal alignment, Person Search, Text based Person Search

Text-based person search has drawn increasing attention due to its wide applications in video surveillance. However, most of the existing models depend heavily on paired image-text data, which is very expensive to acquire. Moreover, they always face huge performance drop when directly exploiting them to new domains. To overcome this problem, we make the first attempt to adapt the model to new target domains in the absence of pairwise labels, which combines the challenges from both cross-modal (text-based) person search and cross-domain person search. Specially, we propose a moment alignment network (MAN) to solve the cross-modal cross-domain person search task in this paper. The idea is to learn three effective moment alignments including domain alignment (DA), cross-modal alignment (CA) and exemplar alignment (EA), which together can learn domain-invariant and semantic aligned cross-modal representations to improve model generalization. Extensive experiments are conducted on CUHK Person Description dataset (CUHK-PEDES) and Richly Annotated Pedestrian dataset (RAP). Experimental results show that our proposed model achieves the state-of-the-art performances on five transfer tasks.
Probabilistic Video Prediction From Noisy Data With a Posterior Confidence
Authors: Yunbo Wang,  Jiajun Wu,  Mingsheng Long,  Joshua B. Tenenbaum
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Prediction, Video Prediction

We study a new research problem of probabilistic future frames prediction from a sequence of noisy inputs, which is useful because it is difficult to guarantee the quality of input frames in practical spatiotemporal prediction applications. It is also challenging because it involves two levels of uncertainty: the perceptual uncertainty from noisy observations and the dynamics uncertainty in forward modeling. In this paper, we propose to tackle this problem with an end-to-end trainable model named Bayesian Predictive Network (BP-Net). Unlike previous work in stochastic video prediction that assumes spatiotemporal coherence and therefore fails to deal with perceptual uncertainty, BP-Net models both levels of uncertainty in an integrated framework. Furthermore, unlike previous work that can only provide unsorted estimations of future frames, BP-Net leverages a differentiable sequential importance sampling (SIS) approach to make future predictions based on the inference of underlying physical states, thereby providing sorted prediction candidates in accordance with the SIS importance weights, i.e., the confidences. Our experiment results demonstrate that BP-Net remarkably outperforms existing approaches on predicting future frames from noisy data.
Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context
Authors: Chenchen Liu,  Yang Jin,  Kehan Xu,  Guoqiang Gong,  Yadong Mu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Relation, Video Visual Relation Detection

Video visual relation detection (VidVRD) aims to describe all interacting objects in a video. Different from relationships in static images, videos contain an addition temporal channel. A majority of existing works divide a video into short segments, predict relationships in each segment, and merge them. Such methods cannot capture relations involving long motions. Predicting the same relationship across neighboring video segments is also inefficient. To address these issues, this work proposes a novel sliding-window scheme to simultaneously predict short-term and long-term relationships. We run windows with different kernel sizes on object tracklets to generate sub-tracklet proposals with different duration, while the computational load is similar to that in segment-based methods. To fully utilize spatial and temporal information in videos, we construct one spatial and one temporal graph and employ Graph Convloutional Network to generate contextual embedding for tracklet proposal compatibility evaluation. We only predict relationships on highly-compatible proposal pairs. Our method achieves state-of-the-art performance on both ImageNet-VidVRD and VidOR dataset across multiple tasks. Especially for ImageNet-VidVRD, we obtain an average of 3% (R@50 from 8.07% to 11.21%) improvement under all evaluation metrics.
RiFeGAN: Rich Feature Generation for Text-to-Image Synthesis From Prior Knowledge
Authors: Jun Cheng,  Fuxiang Wu,  Yanling Tian,  Lei Wang,  Dapeng Tao
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Generation

Text-to-image synthesis is a challenging task that generates realistic images from a textual sequence, which usually contains limited information compared with the corresponding image and so is ambiguous and abstractive. The limited textual information only describes a scene partly, which will complicate the generation with complementing the other details implicitly and lead to low-quality images. To address this problem, we propose a novel rich feature generating text-to-image synthesis, called RiFeGAN, to enrich the given description. In order to provide additional visual details and avoid conflicting, RiFeGAN exploits an attention-based caption matching model to select and refine the compatible candidate captions from prior knowledge. Given enriched captions, RiFeGAN uses self-attentional embedding mixtures to extract features across them effectually and handle the diverging features further. Then it exploits multi-captions attentional generative adversarial networks to synthesize images from those features. The experiments conducted on widely-used datasets show that the models can generate images from enriched captions effectually and improve the results significantly.
Multi-Modality Cross Attention Network for Image and Sentence Matching
Authors: Xi Wei,  Tianzhu Zhang,  Yan Li,  Yongdong Zhang,  Feng Wu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence

The key of image and sentence matching is to accurately measure the visual-semantic similarity between an image and a sentence. However, most existing methods make use of only the intra-modality relationship within each modality or the inter-modality relationship between image regions and sentence words for the cross-modal matching task. Different from them, in this work, we propose a novel MultiModality Cross Attention (MMCA) Network for image and sentence matching by jointly modeling the intra-modality and inter-modality relationships of image regions and sentence words in a unified deep model. In the proposed MMCA, we design a novel cross-attention mechanism, which is able to exploit not only the intra-modality relationship within each modality, but also the inter-modality relationship between image regions and sentence words to complement and enhance each other for image and sentence matching. Extensive experimental results on two standard benchmarks including Flickr30K and MS-COCO demonstrate that the proposed model performs favorably against state-of-the-art image and sentence matching methods.
Learning Augmentation Network via Influence Functions
Authors: Donghoon Lee,  Hyunsin Park,  Trung Pham,  Chang D. Yoo
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification

Data augmentation can impact the generalization performance of an image classification model in a significant way. However, it is currently conducted on the basis of trial and error, and its impact on the generalization performance cannot be predicted during training. This paper considers an influence function that predicts how generalization performance, in terms of validation loss, is affected by a particular augmented training sample. The influence function provides an approximation of the change in validation loss without actually comparing the performances that include and exclude the sample in the training process. Based on this function, a differentiable augmentation network is learned to augment an input training sample to reduce validation loss. The augmented sample is fed into the classification network, and its influence is approximated as a function of the parameters of the last fully-connected layer of the classification network. By backpropagating the influence to the augmentation network, the augmentation network parameters are learned. Experimental results on CIFAR-10, CIFAR-100, and ImageNet show that the proposed method provides better generalization performance than conventional data augmentation methods do.
Deep Structure-Revealed Network for Texture Recognition
Authors: Wei Zhai,  Yang Cao,  Zheng-Jun Zha,  HaiYong Xie,  Feng Wu
Date: 2020-06-01 00:00:00
Proceeding: CVPR 2020 6
Tasks: Semantic Segmentation

Texture recognition is a challenging visual task since various primitives along with their arrangements can be recognized from a same texture image when perceiving with different contexts. Some recent work building on CNNs exploits orderless aggregating to provide invariance to spatial arrangements. However, these methods ignore the inherent structural property of textures, which is a critical cue for distinguishing and describing texture images in the wild. To address this problem, we propose a novel Deep Structure-Revealed Network (DSR-Net) that leverages spatial dependency among the captured primitives as structural representation for texture recognition. Specifically, a primitive capturing module (PCM) is devised to generate multiple primitives from eight directional spatial contexts, in which deep features are firstly extracted under the constrains of direction map and then encoded based on the similarities of neighborhood. Next, these primitives are associated with a dependence learning module (DLM) to generate structural representation, in which a two-way collaborative relationship strategy is introduced to perceive the spatial dependencies among multiple primitives. At last, the structure-revealed texture representations are integrated with spatial ordered information to achieve real-world texture recognition. Evaluation on the five most challenging texture recognition datasets has demonstrated the superiority of the proposed model against state-of-the-art methods. The structure-revealed performances of DSR-Net are further verified on some extensive experiments, including fine-grained classification and semantic segmentation.