pwc-archive/papers-with-abstracts huggingface dataset:
Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization
Authors: Yaoming Wang, Yuchen Liu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Neural Architecture Search

    Existing differentiable neural architecture search approaches simply assume the architectural distribution on each edge is independent of each other, which conflicts with the intrinsic properties of architecture.  In this paper, we view the architectural distribution as the latent representation of specific data points. Then we propose Variational Information Maximization Neural Architecture Search (VIM-NAS) to leverage a simple but effective convolutional neural network to model the latent representation, and optimize for a tractable variational lower bound to the mutual information between the data points and the latent representations. VIM-NAS automatically learns a near one-hot distribution from a continuous distribution with extremely fast convergence speed, e.g., converging with one epoch. Experimental results demonstrate VIM-NAS achieves state-of-the-art performance on various search spaces, including DARTS search space, NAS-Bench-1shot1, NAS-Bench-201, and simplified search spaces S1-S4. Specifically, VIM-NAS achieves a top-1 error rate of 2.45% and 15.80% within 10 minutes on CIFAR-10 and CIFAR-100, respectively, and a top-1 error rate of 24.0% when transferred to ImageNet.
Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces
Authors: Ali Cheraghian, Shafin Rahman, Sameera Ramasinghe, Pengfei Fang, Christian Simon, Lars Petersson, Mehrtash Harandi
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: class-incremental learning, Class Incremental Learning, Few-Shot Class-Incremental Learning, image-classification, Image Classification, Incremental Learning

    Few-shot class incremental learning (FSCIL) aims to incrementally add sets of novel classes to a well-trained base model in multiple training sessions with the restriction that only a few novel instances are available per class. While learning novel classes, FSCIL methods gradually forget base (old) class training and overfit to a few novel class samples. Existing approaches have addressed this problem by computing the class prototypes from the visual or semantic word vector domain. In this paper, we propose addressing this problem using a mixture of subspaces. Subspaces define the cluster structure of the visual domain and help to describe the visual and semantic domain considering the overall distribution of the data. Additionally, we propose to employ a variational autoencoder (VAE) to generate synthesized visual samples for augmenting pseudo-feature while learning novel classes incrementally. The combined effect of the mixture of subspaces and synthesized features reduces the forgetting and overfitting problem of FSCIL. Extensive experiments on three image classification datasets show that our proposed method achieves competitive results compared to state-of-the-art methods.
Localize to Binauralize: Audio Spatialization From Visual Sound Source Localization
Authors: Kranthi Kumar Rachavarapu, Aakanksha, Vignesh Sundaresha, A. N. Rajagopalan
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Audio Generation, Sound Source Localization

    Videos with binaural audios provide an immersive viewing experience by enabling 3D sound sensation. Recent works attempt to generate binaural audio in a multimodal learning framework using large quantities of videos with accompanying binaural audio. In contrast, we attempt a more challenging problem -- synthesizing binaural audios for a video with monaural audio in a weakly supervised setting and weakly semi-supervised setting. Our key idea is that any down-stream task that can be solved only using binaural audios can be used to provide proxy supervision for binaural audio generation, thereby reducing the reliance on explicit supervision. In this work, as a proxy-task for weak supervision, we use Sound Source Localization with only audio. We design a two-stage architecture called Localize-to-Binauralize Network (L2BNet). The first stage of L2BNet is a Stereo Generation (SG) network employed to generate two-stream audio from monaural audio using visual frame information as guidance. In the second stage, an Audio Localization (AL) network is designed to use the synthesized two-stream audio to localize sound sources in visual frames. The entire network is trained end-to-end so that the AL network provides necessary supervision for the SG network. We experimentally show that our weakly-supervised framework generates two-stream audio containing binaural cues. Through user study, we further validate that our proposed approach generates binaural-quality audio using as little as 10% of explicit binaural supervision data for the SG network.
Divide and Conquer for Single-Frame Temporal Action Localization
Authors: Chen Ju, Peisen Zhao, Siheng Chen, Ya zhang, Yanfeng Wang, Qi Tian
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Localization, Temporal Action Localization

    Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the one-stage framework but couple the counting goal and the localization goal. This paper proposes a novel two-stage framework for the STAL task with the spirit of divide and conquer. The instance counting stage leverages the location supervision to determine the number of action instances and divide a whole video into multiple video clips, so that each video clip contains only one complete action instance; and the location estimation stage leverages the category supervision to localize the action instance in each video clip. To efficiently represent the action instance in each video clip, we introduce the proposal-based representation, and design a novel differentiable mask generator to enable the end-to-end training supervised by category labels. On THUMOS14, GTEA, and BEOID datasets, our method outperforms state-of-the-art methods by 3.5%, 2.7%, 4.8% mAP on average. And extensive experiments verify the effectiveness of our method.
Learning From Noisy Data With Robust Representation Learning
Authors: Junnan Li, Caiming Xiong, Steven C.H. Hoi
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Contrastive Learning, Representation Learning

    Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input corruption. In contrast to most existing methods, we combat noise by learning robust representation. Specifically, we embed images into a low-dimensional subspace, and regularize the geometric structure of the subspace with robust contrastive learning, which includes an unsupervised consistency loss and a supervised mixup prototypical loss. We also propose a new noise cleaning method which leverages the learned representation to enforce a smoothness constraint on neighboring samples. Experiments on multiple benchmarks demonstrate state-of-the-art performance of our method and robustness of the learned representation. Code is available at https://github.com/salesforce/RRL/.
Learning High-Fidelity Face Texture Completion Without Complete Face Texture
Authors: Jongyoo Kim, Jiaolong Yang, Xin Tong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Vocal Bursts Intensity Prediction

    For face texture completion, previous methods typically use some complete textures captured by multiview imaging systems or 3D scanners for supervised learning. This paper deals with a new challenging problem -- learning to complete invisible texture in a single face image without using any complete texture. We simply leverage a large corpus of face images of different subjects (e.\,g., FFHQ) to train a texture completion model in an unsupervised manner. To achieve this, we propose DSD-GAN, a novel deep neural network based method that applies two discriminators in UV map space and image space. These two discriminators work in a complementary manner to learn both facial structures and texture details. We show that their combination is essential to obtain high-fidelity results. Despite the network never sees any complete facial appearance, it is able to generate compelling full textures from single images.
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing
Authors: Jae-Han Lee, Chul Lee, Chang-Su Kim
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Multi-Task Learning

    We propose a novel loss weighting algorithm, called loss scale balancing (LSB), for multi-task learning (MTL) of pixelwise vision tasks. An MTL model is trained to estimate multiple pixelwise predictions using an overall loss, which is a linear combination of individual task losses. The proposed algorithm dynamically adjusts the linear weights to learn all tasks effectively. Instead of controlling the trend of each loss value directly, we balance the loss scale --- the product of the loss value and its weight --- periodically. In addition, by evaluating the difficulty of each task based on the previous loss record, the proposed algorithm focuses more on difficult tasks during training. Experimental results show that the proposed algorithm outperforms conventional weighting algorithms for MTL of various pixelwise tasks. Codes are available at https://github.com/jaehanlee-mcl/LSB-MTL.
Image Harmonization With Transformer
Authors: Zonghui Guo, Dongsheng Guo, Haiyong Zheng, Zhaorui Gu, Bing Zheng, Junyu Dong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Decoder, Disentanglement, Image Harmonization, Image Inpainting

    Image harmonization, aiming to make composite images look more realistic, is an important and challenging task. The composite, synthesized by combining foreground from one image with background from another image, inevitably suffers from the issue of inharmonious appearance caused by distinct imaging conditions, i.e., lights. Current solutions mainly adopt an encoder-decoder architecture with convolutional neural network (CNN) to capture the context of composite images, trying to understand what it looks like in the surrounding background near the foreground. In this work, we seek to solve image harmonization with Transformer, by leveraging its powerful ability of modeling long-range context dependencies, for adjusting foreground light to make it compatible with background light while keeping structure and semantics unchanged. We present the design of our harmonization Transformer frameworks without and with disentanglement, as well as comprehensive experiments and ablation study, demonstrating the power of Transformer and investigating the Transformer for vision. Our method achieves state-of-the-art performance on both image harmonization and image inpainting/enhancement, indicating its superiority. Our code and models are available at https://github.com/zhenglab/HarmonyTransformer.
Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification
Authors: Jinrui Yang, Jiawei Zhang, Fufu Yu, Xinyang Jiang, Mengdan Zhang, Xing Sun, Ying-Cong Chen, Wei-Shi Zheng
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Occluded Person Re-Identification, Person Re-Identification

    Person re-identification (ReID) has gained an impressive progress in recent years. However, the occlusion is still a common and challenging problem for recent ReID methods. Several mainstream methods utilize extra cues (e.g., human pose information) to distinguish human parts from obstacles to alleviate the occlusion problem. Although achieving inspiring progress, these methods severely rely on the fine-grained extra cues, and are sensitive to the estimation error in the extra cues. In this paper, we show that existing methods may degrade if the extra information is sparse or noisy. Thus we propose a simple yet effective method that is robust to sparse and noisy pose information. This is achieved by discretizing pose information to the visibility label of body parts, so as to suppress the influence of occluded regions. We show in our experiments that leveraging pose information in this way is more effective and robust. Besides, our method can be embedded into most person ReID models easily. Extensive experiments validate the effectiveness of our model on common occluded person ReID datasets.
Road Anomaly Detection by Partial Image Reconstruction With Segmentation Coupling
Authors: Tomas Vojir, Tomas Sipka, Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino, Jiri Matas
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Anomaly Detection, Autonomous Driving, Image Reconstruction, Segmentation, Semantic Segmentation

    We present a novel approach to the detection of unknown objects in the context of autonomous driving. The problem is formulated as anomaly detection, since we assume that the unknown stuff or object appearance cannot be learned. To that end, we propose a reconstruction module that can be used with many existing semantic segmentation networks, and that is trained to recognize and reconstruct road (drivable) surface from a small bottleneck. We postulate that poor reconstruction of the road surface is due to areas that are outside of the training distribution, which is a strong indicator of an anomaly. The road structural similarity error is coupled with the semantic segmentation to incorporate information from known classes and produce final per-pixel anomaly scores. The proposed JSR-Net was evaluated on four datasets, Lost-and-found, Road Anomaly, Road Obstacles, and FishyScapes, achieving state-of-art performance on all, reducing the false positives significantly, while typically having the highest average precision for wide range of operation points.
DeepPRO: Deep Partial Point Cloud Registration of Objects
Authors: Donghoon Lee, Onur C. Hamsici, Steven Feng, Prachee Sharma, Thorsten Gernoth
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Point Cloud Registration

    We consider the problem of online and real-time registration of partial point clouds obtained from an unseen real-world rigid object without knowing its 3D model. The point cloud is partial as it is obtained by a depth sensor capturing only the visible part of the object from a certain viewpoint. It introduces two main challenges: 1) two partial point clouds do not fully overlap and 2) keypoints tend to be less reliable when the visible part of the object does not have salient local structures. To address these issues, we propose DeepPRO, a keypoint-free and an end-to-end trainable deep neural network. Its core idea is inspired by how humans align two point clouds: we can imagine how two point clouds will look like after the registration based on their shape. To realize the idea, DeepPRO has inputs of two partial point clouds and directly predicts the point-wise location of the aligned point cloud. By preserving the ordering of points during the prediction, we enjoy dense correspondences between input and predicted point clouds when inferring rigid transform parameters. We conduct extensive experiments on the real-world Linemod and synthetic ModelNet40 datasets. In addition, we collect and evaluate on the PRO1k dataset, a large-scale version of Linemod meant to test generalization to real-world scans. Results show that DeepPRO achieves the best accuracy against thirteen strong baseline methods, e.g., 2.2mm ADD on the Linemod dataset, while running 50 fps on mobile devices.
Self-Supervised Vessel Segmentation via Adversarial Learning
Authors: Yuxin Ma, Yang Hua, Hanming Deng, Tao Song, Hao Wang, Zhengui Xue, Heng Cao, Ruhui Ma, Haibing Guan
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Segmentation

    Vessel segmentation is critically essential for diagnosinga series of diseases, e.g., coronary artery disease and retinal disease. However, annotating vessel segmentation maps of medical images is notoriously challenging due to the tiny and complex vessel structures, leading to insufficient available annotated datasets for existing supervised methods and domain adaptation methods. The subtle structures and confusing background of medical images further suppress the efficacy of unsupervised methods. In this paper, we propose a self-supervised vessel segmentation method via adversarial learning. Our method learns vessel representations by training an attention-guided generator and a segmentation generator to simultaneously synthesize fake vessels and segment vessels out of coronary angiograms. To support the research, we also build the first X-ray angiography coronary vessel segmentation dataset, named XCAD. We evaluate our method extensively on multiple vessel segmentation datasets, including the XCAD dataset, the DRIVE dataset,and the STARE dataset. The experimental results show our method suppresses unsupervised methods significantly and achieves competitive performance compared with supervised methods and traditional methods.
Polarimetric Helmholtz Stereopsis
Authors: Yuqi Ding, Yu Ji, Mingyuan Zhou, Sing Bing Kang, Jinwei Ye
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Reconstruction

    Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i.e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance. In this paper, we present the polarimetric Helmholtz stereopsis (polar-HS), which extends the classical HS by considering the polarization state of light in the reciprocal paths. With the additional phase information from polarization, polar-HS requires only one reciprocal image pair. We formulate new reciprocity and diffuse/specular polarimetric constraints to recover surface depths and normals using an optimization framework. Using a hardware prototype, we show that our approach produces high-quality 3D reconstruction for different types of surfaces, ranging from diffuse to highly specular.
A General Recurrent Tracking Framework Without Real Data
Authors: Shuai Wang, Hao Sheng, Yang Zhang, Yubin Wu, Zhang Xiong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Multi-Object Tracking, Object Tracking

    Recent progress in multi-object tracking (MOT) has shown great significance of a robust scoring mechanism for potential tracks. However, the lack of available data in MOT makes it difficult to learn a general scoring mechanism. Multiple cues including appearance, motion and etc., are limitedly utilized in current manual scoring functions. In this paper, we propose a Multiple Nodes Tracking (MNT) framework that adapts to most trackers. Based on this framework, a Recurrent Tracking Unit (RTU) is designed to score potential tracks through long-term information. In addition, we present a method of generating simulated tracking data without real data to overcome the defect of limited available data in MOT. The experiments demonstrate that our simulated tracking data is effective for training RTU and achieves state-of-the-art performance on both MOT17 and MOT16 benchmarks. Meanwhile, RTU can be flexibly plugged into classic trackers such as DeepSORT and MHT, and makes remarkable improvements as well.
TravelNet: Self-Supervised Physically Plausible Hand Motion Learning From Monocular Color Images
Authors: Zimeng Zhao, Xi Zhao, Yangang Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Decoder, Self-Supervised Learning

    This paper aims to reconstruct physically plausible hand motion from monocular color images. Existing frame-by-frame estimating approaches can not guarantee the physical plausibility (e.g. penetration, jittering) directly. In this paper, we embed physical constraints on the per-frame estimated motions in both spatial and temporal space. Our key idea is to adopt a self-supervised learning strategy to train a novel encoder-decoder, named TravelNet, whose training motion data is prepared by the physics engine using discrete pose states. TravelNet captures key pose states from hand motion sequences as compact motion descriptors, inspired by the concept of keyframes in animation. Finally, it manages to extract those key states out of perturbations without manual annotations, and reconstruct the motions preserving details and physical plausibility. In the experiments, we show that the outputs of the TravelNet contain both finger synergism and time consistency. Through the proposed framework, hand motions can be accurately reconstructed and flexibly re-edited, which is superior to the state-of-the-art methods.
Knowledge Mining and Transferring for Domain Adaptive Object Detection
Authors: Kun Tian, Chenghao Zhang, Ying Wang, Shiming Xiang, Chunhong Pan
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Attribute, Domain Adaptation, Object, object-detection, Object Detection, Transfer Learning

    With the thriving of deep learning, CNN-based object detectors have made great progress in the past decade. However, the domain gap between training and testing data leads to a prominent performance degradation and thus hinders their application in the real world. To alleviate this problem, Knowledge Transfer Network (KTNet) is proposed as a new paradigm for domain adaption. Specifically, KTNet is constructed on a base detector with intrinsic knowledge mining and relational knowledge constraints. First, we design a foreground/background classifier shared by source domain and target domain to extract the common attribute knowledge of objects in different scenarios. Second, we model the relational knowledge graph and explicitly constrain the consistency of category correlation under source domain, target domain, as well as cross-domain conditions. As a result, the detector is guided to learn object-related and domain-independent representation. Extensive experiments and visualizations confirm that transferring object-specific knowledge can yield notable performance gains. The proposed KTNet achieves state-of-the-art results on three cross-domain detection benchmarks.
FashionMirror: Co-Attention Feature-Remapping Virtual Try-On With Sequential Template Poses
Authors: Chieh-Yun Chen, Ling Lo, Pin-Jui Huang, Hong-Han Shuai, Wen-Huang Cheng
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Segmentation, Semantic Segmentation, Virtual Try-on

    Virtual try-on tasks have drawn increased attention. Prior arts focus on tackling this task via warping clothes and fusing the information at the pixel level with the help of semantic segmentation. However, conducting semantic segmentation is time-consuming and easily causes error accumulation over time. Besides, warping the information at the pixel level instead of the feature level limits the performance (e.g., unable to generate different views) and is unstable since it directly demonstrates the results even with a misalignment. In contrast, fusing information at the feature level can be further refined by the convolution to obtain the final results. Based on these assumptions, we propose a co-attention feature-remapping framework, namely FashionMirror, that generates the try-on results according to the driven-pose sequence in two stages. In the first stage, we consider the source human image and the target try-on clothes to predict the removed mask and the try-on clothing mask, which replaces the pre-processed semantic segmentation and reduces the inference time. In the second stage, we first remove the clothes on the source human via the removed mask and warp the clothing features conditioning on the try-on clothing mask to fit the next frame human. Meanwhile, we predict the optical flows from the consecutive 2D poses and warp the source human to the next frame at the feature level. Then, we enhance the clothing features and source human features in every frame to generate realistic try-on results with spatio-temporal smoothness. Both qualitative and quantitative results show that FashionMirror outperforms the state-of-the-art virtual try-on approaches.