pwc-archive/papers-with-abstracts huggingface dataset:
Generating Manga From Illustrations via Mimicking Manga Creation Workflow
Authors: Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, Chunping Liu
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1

    We present a framework to generate manga from digital illustrations. In professional mange studios, the manga create workflow consists of three key steps: (1) Artists use line drawings to delineate the structural outlines in manga storyboards. (2) Artists apply several types of regular screentones to render the shading, occlusion, and object materials. (3) Artists selectively paste irregular screen textures onto the canvas to achieve various background layouts or special effects. Motivated by this workflow, we propose a data-driven framework to convert a digital illustration into three corresponding components: manga line drawing, regular screentone, and irregular screen texture. These components can be directly composed into manga images and can be further retouched for more plentiful manga creations. To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner. We conduct both perceptual user study and qualitative evaluation of the generated manga, and observe that our generated image layers for these three components are practically usable in the daily works of manga artists. We provide 60 qualitative results and 15 additional comparisons in the supplementary material. We will make our presented manga dataset publicly available to assist related applications.
Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation
Authors: Mengyao Zhai, Lei Chen, Greg Mori
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Continual Learning, Lifelong learning

    Deep neural networks are susceptible to catastrophic forgetting: when encountering a new task, they can only remember the new task and fail to preserve its ability to accomplish previously learned tasks. In this paper, we study the problem of lifelong learning for generative models and propose a novel and generic continual learning framework Hyper-LifelongGAN which is more scalable compared with state-of-the-art approaches. Given a sequence of tasks, the conventional convolutional filters are factorized into the dynamic base filters which are generated using task specific filter generators, and deterministic weight matrix which linearly combines the base filters and is shared across different tasks. Moreover, the shared weight matrix is multiplied by task specific coefficients to introduce more flexibility in combining task specific base filters differently for different tasks. Attributed to the novel architecture, the proposed method can preserve or even improve the generation quality at a low cost of parameters. We validate Hyper-LifelongGAN on diverse image-conditioned generation tasks, extensive ablation studies and comparisons with state-of-the-art models are carried out to show that the proposed approach can address catastrophic forgetting effectively.
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs
Authors: Lile Cai, Xun Xu, Jun Hao Liew, Chuan Sheng Foo
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Active Learning, Semantic Segmentation, Superpixels

    State-of-the-art methods for semantic segmentation are based on deep neural networks that are known to be data-hungry. Region-based active learning has shown to be a promising method for reducing data annotation costs. A key design choice for region-based AL is whether to use regularly-shaped regions (e.g., rectangles) or irregularly-shaped region (e.g., superpixels). In this work, we address this question under realistic, click-based measurement of annotation costs. In particular, we revisit the use of superpixels and demonstrate that the inappropriate choice of cost measure (e.g., the percentage of labeled pixels), may cause the effectiveness of the superpixel-based approach to be under-estimated. We benchmark the superpixel-based approach against the traditional "rectangle+polygon"-based approach with annotation cost measured in clicks, and show that the former outperforms on both Cityscapes and PASCAL VOC. We further propose a class-balanced acquisition function to boost the performance of the superpixel-based approach and demonstrate its effectiveness on the evaluation datasets. Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.
Prototype-Guided Saliency Feature Learning for Person Search
Authors: Hanjae Kim, Sunghun Joung, Ig-Jae Kim, Kwanghoon Sohn
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Human Detection, Person Search

    Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system. Though promising results have been achieved, the misalignment problem, which commonly occurs in person search, limits the discriminative feature representation for re-ID. To overcome this limitation, we introduce a novel framework to learn the discriminative representation by utilizing prototype in OIM loss. Unlike conventional methods using prototype as a representation of person identity, we utilize it as guidance to allow the attention network to consistently highlight multiple instances across different poses. Moreover, we propose a new prototype update scheme with adaptive momentum to increase the discriminative ability across different instances. Extensive ablation experiments demonstrate that our method can significantly enhance the feature discriminative power, outperforming the state-of-the-art results on two person search benchmarks including CUHK-SYSU and PRW.
Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval
Authors: Yawen Zeng, Da Cao, Xiaochi Wei, Meng Liu, Zhou Zhao, Zheng Qin
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Cross-Modal Retrieval, Graph Matching, Moment Retrieval, Relation, Retrieval, Sentence

    Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactions or alignments between different modalities. However, the task of cross-modal video moment retrieval is not yet thoroughly addressed as it needs to further identify the fine-grained differences of video moment candidates with high repeatability and similarity. Moveover, the relation among objects in both video and query sentence is intuitive and efficient for understanding semantics but is rarely considered. Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates. Specifically, we first introduce a visual relational graph and a textual relational graph to form relation-aware representations via message propagation. Thereafter, a multi-task pre-training is designed to capture domain-specific knowledge about objects and relations, enhancing the structured visual representation after explicitly defined relation. Finally, the graph matching and boundary regression are employed to perform the cross-modal retrieval. We conduct extensive experiments on two datasets about daily activities and cooking activities, demonstrating significant improvements over state-of-the-art solutions.
Strengthen Learning Tolerance for Weakly Supervised Object Localization
Authors: Guangyu Guo, Junwei Han, Fang Wan, Dingwen Zhang
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Object, Object Localization, Weakly-Supervised Object Localization

    Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision. While numerous efforts have been made in this field, recent approaches still suffer from two challenges: one is the part domination issue while the other is the learning robustness issue. Specifically, the former makes the localizer prone to the local discriminative object regions rather than the desired whole object, and the latter makes the localizer over-sensitive to the variations of the input images so that one can hardly obtain localization results robust to the arbitrary visual stimulus. To solve these issues, we propose a novel framework to strengthen the learning tolerance, referred to as SLT-Net, for WSOL. Specifically, we consider two-fold learning tolerance strengthening mechanisms. One is the semantic tolerance strengthening mechanism, which allows the localizer to make mistakes for classifying similar semantics so that it will not concentrate too much on the discriminative local regions. The other is the visual stimuli tolerance strengthening mechanism, which enforces the localizer to be robust to different image transformations so that the prediction quality will not be sensitive to each specific input image. Finally, we implement comprehensive experimental comparisons on two widely-used datasets CUB and ILSVRC2012, which demonstrate the effectiveness of our proposed approach.
RankDetNet: Delving Into Ranking Constraints for Object Detection
Authors: Ji Liu, Dong Li, Rongzhang Zheng, Lu Tian, Yi Shan
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Object Detection, Classification, Learning-To-Rank, Object, object-detection, Object Detection

    Modern object detection approaches cast detecting objects as optimizing two subtasks of classification and localization simultaneously. Existing methods often learn the classification task by optimizing each proposal separately and neglect the relationship among different proposals. Such detection paradigm also encounters the mismatch between classification and localization due to the inherent discrepancy of their optimization targets. In this work, we propose a ranking-based optimization algorithm for harmoniously learning to rank and localize proposals in lieu of the classification task. To this end, we comprehensively investigate three types of ranking constraints, i.e., global ranking, class-specific ranking and IoU-guided ranking losses. The global ranking loss encourages foreground samples to rank higher than background. The class-specific ranking loss ensures that positive samples rank higher than negative ones for each specific class. The IoU-guided ranking loss aims to align each pair of confidence scores with the associated pair of IoU overlap between two positive samples of a specific class. Our ranking constraints can sufficiently explore the relationships between samples from three different perspectives. They are easy-to-implement, compatible with mainstream detection frameworks and computation-free for inference. Experiments demonstrate that our RankDetNet consistently surpasses prior anchor-based and anchor-free baselines, e.g., improving RetinaNet baseline by 2.5% AP on the COCO test-dev set without bells and whistles. We also apply the proposed ranking constraints for 3D object detection and achieve improved performance, which further validates the superiority and generality of our method.
Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources
Authors: Pengyu Li, Biao Wang, Lei Zhang
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Face Recognition, GPU, Metric Learning

    Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited computational resources is still a challenge. This is because the classification paradigm needs to train a fully connected layer as the category classifier, and its parameters will be in the hundreds of millions if the training dataset contains millions of identities. This requires many computational resources, such as GPU memory. The metric learning paradigm is an economical computation method, but its performance is greatly inferior to that of the classification paradigm. To address this challenge, we propose a simple but effective CNN layer called the Virtual fully connected (Virtual FC) layer to reduce the computational consumption of the classification paradigm. Without bells and whistles, the proposed Virtual FC reduces the parameters by more than 100 times with respect to the fully connected layer and achieves competitive performance on mainstream face recognition evaluation datasets. Moreover, the performance of our Virtual FC layer on the evaluation datasets is superior to that of the metric learning paradigm by a significant margin. Our code will be released in hopes of disseminating our idea to other domains.
Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification
Authors: Peixian Hong, Tao Wu, AnCong Wu, Xintong Han, Wei-Shi Zheng
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Cloth-Changing Person Re-Identification, Person Re-Identification

    Recently, person re-identification (Re-ID) has achieved great progress. However, current methods largely depend on color appearance, which is not reliable when a person changes the clothes. Cloth-changing Re-ID is challenging since pedestrian images with clothes change exhibit large intra-class variation and small inter-class variation. Some significant features for identification are embedded in unobvious body shape differences across pedestrians. To explore such body shape cues for cloth-changing Re-ID, we propose a Fine-grained Shape-Appearance Mutual learning framework (FSAM), a two-stream framework that learns fine-grained discriminative body shape knowledge in a shape stream and transfers it to an appearance stream to complement the cloth-unrelated knowledge in the appearance features. Specifically, in the shape stream, FSAM learns fine-grained discriminative mask with the guidance of identities and extracts fine-grained body shape features by a pose-specific multi-branch network. To complement cloth-unrelated shape knowledge in the appearance stream, dense interactive mutual learning is performed across low-level and high-level features to transfer knowledge from shape stream to appearance stream, which enables the appearance stream to be deployed independently without extra computation for mask estimation. We evaluated our method on benchmark cloth-changing Re-ID datasets and achieved the start-of-the-art performance.
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence
Authors: Jae Yong Lee, Joseph DeGol, Victor Fragoso, Sudipta N. Sinha
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Semantic correspondence

    We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently the best methods for this task. However, these architectures require exhaustive matching and 4D convolutions over matching costs for all pairs of feature map pixels. This makes them computationally expensive. We present a more efficient neighborhood consensus approach based on PatchMatch. For higher accuracy, we propose to use a learned local 4D scoring function for evaluating candidates during the PatchMatch iterations. We have devised an approach to jointly train the scoring function and the feature extraction modules by embedding them into a proxy model which is end-to-end differentiable. The modules are trained in a supervised setting using a cross-entropy loss to directly incorporate sparse keypoint supervision. Our evaluation on PF-Pascal and SPair-71K shows that our method significantly outperforms the state-of-the-art on both datasets while also being faster and using less memory.
Data-Free Knowledge Distillation for Image Super-Resolution
Authors: Yiman Zhang, Hanting Chen, Xinghao Chen, Yiping Deng, Chunjing Xu, Yunhe Wang
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Data-free Knowledge Distillation, Image Super-Resolution, Knowledge Distillation, Model Compression, Super-Resolution

    Convolutional network compression methods require training data for achieving acceptable results, but training data is routinely unavailable due to some privacy and transmission limitations. Therefore, recent works focus on learning efficient networks without original training data, i.e., data-free model compression. Wherein, most of existing algorithms are developed for image recognition or segmentation tasks. In this paper, we study the data-free compression approach for single image super-resolution (SISR) task which is widely used in mobile phones and smart cameras. Specifically, we analyze the relationship between the outputs and inputs from the pre-trained network and explore a generator with a series of loss functions for maximally capturing useful information. The generator is then trained for synthesizing training samples which have similar distribution to that of the original data. To further alleviate the training difficulty of the student network using only the synthetic data, we introduce a progressive distillation scheme. Experiments on various datasets and architectures demonstrate that the proposed method is able to be utilized for effectively learning portable student networks without the original data, e.g., with 0.16dB PSNR drop on Set5 for x2 super resolution. Code will be available at https://github.com/huaweinoah/Data-Efficient-Model-Compression.
Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations
Authors: Zhihui Li, Lina Yao
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Action Detection, Action Localization, Multi-Task Learning, Region Proposal, Temporal Action Localization

    Temporal action detection on unconstrained videos has seen significant research progress in recent years. Deep learning has achieved enormous success in this direction. However, collecting large-scale temporal detection datasets to ensuring promising performance in the real-world is a laborious, impractical and time consuming process. Accordingly, we present a novel improved temporal action localization model that is better able to take advantage of limited labeled data available. Specifically, we design two auxiliary tasks by reconstructing the available label information and then facilitate the learning of the temporal action detection model. Each task generates their supervision signal by recycling the original annotations, and are jointly trained with the temporal action detection model in a multi-task learning fashion. Note that the proposed approach can be pluggable to any region proposal based temporal action detection models. We conduct extensive experiments on three benchmark datasets, namely THUMOS'14, Charades and ActivityNet. Our experimental results confirm the effectiveness of the proposed model.
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals
Authors: Kun Qian, Shilin Zhu, Xinyu Zhang, Li Erran Li
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Autonomous Driving, vehicle detection

    Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving. While they generate fine-grained point clouds or high-resolution images with rich information in good weather conditions, they fail in adverse weather (e.g., fog) where opaque particles distort lights and significantly reduce visibility. Thus, existing methods relying on lidar or camera experience significant performance degradation in rare but critical adverse weather conditions. To remedy this, we resort to exploiting complementary radar, which is less impacted by adverse weather and becomes prevalent on vehicles. In this paper, we present Multimodal Vehicle Detection Network (MVDNet), a two-stage deep fusion detector, which first generates proposals from two sensors and then fuses region-wise features between multimodal sensor streams to improve final detection results. To evaluate MVDNet, we create a procedurally generated training dataset based on the collected raw lidar and radar signals from the open-source Oxford Radar Robotcar. We show that the proposed MVDNet surpasses other state-of-the-art methods, notably in terms of Average Precision (AP), especially in adverse weather conditions. The code and data are available at https://github.com/qiank10/MVDNet.
Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling
Authors: Daniel Stadler, Jurgen Beyerer
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Management, Occlusion Handling

    Multi-pedestrian trackers perform well when targets are clearly visible making the association task quite easy. However, when heavy occlusions are present, a mechanism to reidentify persons is needed. The common approach is to extract visual features from new detections and compare them with the features of previously found tracks. Since those detections can have substantial overlaps with nearby targets - especially in crowded scenarios - the extracted features are insufficient for a reliable re-identification. In contrast, we propose a novel occlusion handling strategy that explicitly models the relation between occluding and occluded tracks outperforming the feature-based approach, while not depending on a separate re-identification network. Furthermore, we improve the track management of a regression-based method in order to bypass missing detections and to deal with tracks leaving the scene at the border of the image. Finally, we apply our tracker in both temporal directions and merge tracklets belonging to the same target, which further enhances the performance. We demonstrate the effectiveness of our tracking components with ablative experiments and surpass the state-of-the-art methods on the three popular pedestrian tracking benchmarks MOT16, MOT17, and MOT20.
Self-Supervised 3D Mesh Reconstruction From Single Images
Authors: Tao Hu, LiWei Wang, Xiaogang Xu, Shu Liu, Jiaya Jia
Date: 2021-06-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Reconstruction, Attribute, Image Generation, Single-View 3D Reconstruction

    Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation. However, without explicit 3D attribute-level supervision, it is still difficult to achieve satisfying reconstruction accuracy. In this paper, we propose a Self-supervised Mesh Reconstruction (SMR) approach to enhance 3D mesh attribute learning process. Our approach is motivated by observations that (1) 3D attributes from interpolation and prediction should be consistent, and (2) feature representation of landmarks from all images should be consistent. By only requiring silhouette mask annotation, our SMR can be trained in an end-to-end manner and generalizes to reconstruct natural objects of birds, cows, motorbikes, etc. Experiments demonstrate that our approach improves both 2D supervised and unsupervised 3D mesh reconstruction on multiple datasets. We also show that our model can be adapted to other image synthesis tasks, e.g., novel view generation, shape transfer, and texture transfer, with promising results. Our code is publicly available at https://github.com/Jia-Research-Lab.