pwc-archive/papers-with-abstracts huggingface dataset:
4D Cloud Scattering Tomography
Authors: Roi Ronen, Yoav Y. Schechner, Eshkol Eytan
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Computed Tomography (CT), Object

    We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth's climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. Spatiotemporal CT is achieved using gradient-based optimization, which accounts for the correlation time of the dynamic object content. We demonstrate this in physics-based simulations and on experimental real-world data.
PU-EVA: An Edge-Vector Based Approximation Solution for Flexible-Scale Point Cloud Upsampling
Authors: Luqing Luo, Lulu Tang, Wanyi Zhou, Shizheng Wang, Zhi-Xin Yang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: point cloud upsampling, Surface Reconstruction

    High-quality point clouds have practical significance for point-based rendering, semantic understanding, and surface reconstruction. Upsampling sparse, noisy and non-uniform point clouds for a denser and more regular approximation of target objects is a desirable but challenging task. Most existing methods duplicate point features for upsampling, constraining the upsampling scales at a fixed rate. In this work, the arbitrary point clouds upsampling rates are achieved via edge-vector based affine combinations, and a novel design of Edge-Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed. The edge-vector based approximation encodes neighboring connectivity via affine combinations based on edge vectors, and restricts the approximation error within a second-order term of Taylor's Expansion. Moreover, the EVA upsampling decouples the upsampling scales with network architecture, achieving the arbitrary upsampling rates in one-time training. Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA outperforms the state-of-the-arts in terms of proximity-to-surface, distribution uniformity, and geometric details preservation.
Robust Watermarking for Deep Neural Networks via Bi-Level Optimization
Authors: Peng Yang, Yingjie Lao, Ping Li
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10

    Deep neural networks (DNNs) have become state-of-the-art in many application domains. The increasing complexity and cost for building these models demand means for protecting their intellectual property (IP). This paper presents a novel DNN framework that optimizes the robustness of the embedded watermarks. Our method is originated from DNN fault attacks. Different from prior end-to-end DNN watermarking approaches, we only modify a tiny subset of weights to embed the watermark, which also facilities better control of the model behaviors and enables larger rooms for optimizing the robustness of the watermarks. In this paper, built upon the above concept, we propose a bi-level optimization framework where the inner loop phase optimizes the example-level problem to generate robust exemplars, while the outer loop phase proposes a masked adaptive optimization to achieve the robustness of the projected DNN models. Our method alternates the learning of the protected models and watermark exemplars across all phases, where watermark exemplars are not just data samples that could be optimized and adjusted instead. We verify the performance of the proposed methods over a wide range of datasets and DNN architectures. Various transformation attacks including fine-tuning, pruning and overwriting are used to evaluate the robustness.
Pano-AVQA: Grounded Audio-Visual Question Answering on 360deg Videos
Authors: Heeseung Yun, Youngjae Yu, Wonsuk Yang, Kangil Lee, Gunhee Kim
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Audio-visual Question Answering, Question Answering, Relation, Visual Question Answering, Visual Question Answering (VQA)

    360deg videos convey holistic views for the surroundings of a scene. It provides audio-visual cues beyond predetermined normal field of views and displays distinctive spatial relations on a sphere. However, previous benchmark tasks for panoramic videos are still limited to evaluate the semantic understanding of audio-visual relationships or spherical spatial property in surroundings. We propose a novel benchmark named Pano-AVQA as a large-scale grounded audio-visual question answering dataset on panoramic videos. Using 5.4K 360deg video clips harvested online, we collect two types of novel question-answer pairs with bounding-box grounding: spherical spatial relation QAs and audio-visual relation QAs. We train several transformer-based models from Pano-AVQA, where the results suggest that our proposed spherical spatial embeddings and multimodal training objectives fairly contribute to better semantic understanding of the panoramic surroundings on the dataset.
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
Authors: Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, Yong liu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Point Cloud Completion

    Point cloud completion is an interesting and challenging task in 3D vision, aiming to recover complete shapes from sparse and incomplete point clouds. Existing learning-based methods often require vast computation cost to achieve excellent performance, which limits their practical applications. In this paper, we propose a novel Recurrent Forward Network (RFNet), which is composed of three modules: Recurrent Feature Extraction (RFE), Forward Dense Completion (FDC) and Raw Shape Protection (RSP). The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline. The RSP introduces details from the original incomplete models to refine the completion results. Besides, we propose a Sampling Chamfer Distance to better capture the shapes of models and a new Balanced Expansion Constraint to restrict the expansion distances from coarse to fine. According to the experiments on ShapeNet and KITTI, our network can achieve the state-of-the-art with lower memory cost and faster convergence.
PIAP-DF: Pixel-Interested and Anti Person-Specific Facial Action Unit Detection Net With Discrete Feedback Learning
Authors: Yang Tang, Wangding Zeng, Dafei Zhao, Honggang Zhang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Unit Detection, Facial Action Unit Detection

    Facial Action Units (AUs) are of great significance in communication. Automatic AU detection can improve the understanding of psychological conditions and emotional status. Recently, several deep learning methods have been proposed to detect AUs automatically. However, several challenges, such as poor extraction of fine-grained and robust local AUs information, model overfitting on person-specific features, as well as the limitation of datasets with wrong labels, remain to be addressed. In this paper, we propose a joint strategy called PIAP-DF to solve these problems, which involves 1) a multi-stage Pixel-Interested learning method with pixel-level attention for each AU; 2) an Anti Person-Specific method aiming to eliminate features associated with any individual as much as possible; 3) a semi-supervised learning method with Discrete Feedback, designed to effectively utilize unlabeled data and mitigate the negative impacts of wrong labels. Experimental results on the two popular AU detection datasets BP4D and DISFA prove that PIAP-DF can be the new state-of-the-art method. Compared with the current best method, PIAP-DF improves the average F1 score by 3.2% on BP4D and by 0.5% on DISFA. All modules of PIAP-DF can be easily removed after training to obtain a lightweight model for practical application.
Detecting Human-Object Relationships in Videos
Authors: Jingwei Ji, Rishi Desai, Juan Carlos Niebles
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Human-Object Relationship Detection, Object, Relationship Detection

    We study a crucial problem in video analysis: human-object relationship detection. The majority of previous approaches are developed only for the static image scenario, without incorporating the temporal dynamics so vital to contextualizing human-object relationships. We propose a model with Intra- and Inter-Transformers, enabling joint spatial and temporal reasoning on multiple visual concepts of objects, relationships, and human poses. We find that applying attention mechanisms among features distributed spatio-temporally greatly improves our understanding of human-object relationships. Our method is validated on two datasets, Action Genome and CAD-120-EVAR, and achieves state-of-the-art performance on both of them.
PrimitiveNet: Primitive Instance Segmentation With Local Primitive Embedding Under Adversarial Metric
Authors: Jingwei Huang, Yanfeng Zhang, Mingwei Sun
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

    We present PrimitiveNet, a novel approach for high-resolution primitive instance segmentation from point clouds on a large scale. Our key idea is to transform the global segmentation problem into easier local tasks. We train a high-resolution primitive embedding network to predict explicit geometry features and implicit latent features for each point. The embedding is jointly trained with an adversarial network as a primitive discriminator to decide whether points are from the same primitive instance in local neighborhoods. Such local supervision encourages the learned embedding and discriminator to describe local surface properties and robustly distinguish different instances. At inference time, network predictions are followed by a region growing method to finalize the segmentation. Experiments show that our method outperforms existing state-of-the-arts based on mean average precision by a significant margin (46.3%) on ABC dataset [??]. We can process extremely large real scenes covering more than 0.1km^2. Ablation studies highlight the contribution of our core designs. Finally, our method can improve geometry processing algorithms to abstract scans as lightweight models.
Transforms Based Tensor Robust PCA: Corrupted Low-Rank Tensors Recovery via Convex Optimization
Authors: Canyi Lu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10

    This work studies the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our model is motivated by the recently proposed linear transforms based tensor-tensor product and tensor SVD. We define a new transforms depended tensor rank and the corresponding tensor nuclear norm. Then we solve the TRPCA problem by convex optimization whose objective is a weighted combination of the new tensor nuclear norm and l_1-norm. In theory, we prove that under some incoherence conditions, the convex program exactly recovers the underlying low-rank and sparse components with high probability. Our new TRPCA is much more general since it allows to use any invertible linear transforms. Thus, we have more choices in practice for different tasks and different type of data. Numerical experiments verify our results and the application on image recovery demonstrates the superiority of our method.
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher
Authors: Yichen Zhu, Yi Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: image-classification, Image Classification, Knowledge Distillation, Multi-Task Learning, object-detection, Object Detection, Semantic Segmentation

    Knowledge distillation (KD) transfers the dark knowledge from cumbersome networks (teacher) to lightweight (student) networks and expects the student to achieve more promising performance than training without the teacher's knowledge. However, a counter-intuitive argument is that better teachers do not make better students due to the capacity mismatch. To this end, we present a novel adaptive knowledge distillation method to complement traditional approaches. The proposed method, named as Student Customized Knowledge Distillation (SCKD), examines the capacity mismatch between teacher and student from the perspective of gradient similarity. We formulate the knowledge distillation as a multi-task learning problem so that the teacher transfers knowledge to the student only if the student can benefit from learning such knowledge. We validate our methods on multiple datasets with various teacher-student configurations on image classification, object detection, and semantic segmentation.
Meta Pairwise Relationship Distillation for Unsupervised Person Re-Identification
Authors: Haoxuanye Ji, Le Wang, Sanping Zhou, Wei Tang, Nanning Zheng, Gang Hua
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Person Re-Identification, Unsupervised Person Re-Identification

    Unsupervised person re-identification (Re-ID) remains challenging due to the lack of ground-truth labels. Existing methods often rely on estimated pseudo labels via iterative clustering and classification, and they are unfortunately highly susceptible to performance penalties incurred by the inaccurate estimated number of clusters. Alternatively, we propose the Meta Pairwise Relationship Distillation (MPRD) method to estimate the pseudo labels of sample pairs for unsupervised person Re-ID. Specifically, it consists of a Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN), in which the GCN estimates the pseudo labels of sample pairs based on the current features extracted by CNN, and the CNN learns better features by involving high-fidelity positive and negative sample pairs imposed by GCN. To achieve this goal, a small amount of labeled samples are used to guide GCN training, which can distill meta knowledge to judge the difference in the neighborhood structure between positive and negative sample pairs. Extensive experiments on Market-1501, DukeMTMC-reID and MSMT17 datasets show that our method outperforms the state-of-the-art approaches.
Cross-Patch Graph Convolutional Network for Image Denoising
Authors: Yao Li, Xueyang Fu, Zheng-Jun Zha
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Deep Learning, Denoising, Image Denoising

    Recently, deep learning-based image denoising methods have achieved significant improvements over traditional methods. Due to the hardware limitation, most deep learning-based image denoising methods utilize cropped small patches to train a convolutional neural network to infer the clean images. However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image. In this paper, we propose Cross-Patch Net (CPNet), which is the first deep- learning-based real image denoising method for HR (high resolution) input. Furthermore, we design a novel loss guided by the noise level map to obtain better performance. Compared with the vanilla patch-based training strategies, our approach effectively exploits the cross-patch contextual dependency. effective method to generate realistic sRGB noisy images from their corresponding clean sRGB images for denoiser training. Denoising experiments on real-world sRGB images show the effectiveness of the proposed method. More importantly, our method achieves state-of-the-art performance on practical sRGB noisy image denoising.
Online-Trained Upsampler for Deep Low Complexity Video Compression
Authors: Jan P. Klopp, Keng-Chi Liu, Shao-Yi Chien, Liang-Gee Chen
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 8k, Decoder, Video Compression

    Deep learning for image and video compression has demonstrated promising results both as a standalone technology and a hybrid combination with existing codecs. However, these systems still come with high computational costs. Deep learning models are typically applied directly in pixel space, making them expensive when resolutions become large. In this work, we propose an online-trained upsampler to augment an existing codec. The upsampler is a small neural network trained on an isolated group of frames. Its parameters are signalled to the decoder. This hybrid solution has a small scope of only 10s or 100s of frames and allows for a low complexity both on the encoding and the decoding side. Our algorithm works in offline and in zero-latency settings. Our evaluation employs the popular x265 codec on several high-resolution datasets ranging from Full HD to 8K. We demonstrate rate savings between 8.6% and 27.5% and provide ablation studies to show the impact of our design decisions. In comparison to similar works, our approach performs favourably.
T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning
Authors: Zhen Zhong, Guobao Xiao, Linxin Zheng, Yan Lu, Jiayi Ma
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Vocal Bursts Valence Prediction

    We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning. Given a set of putative correspondences, we reject outliers and regress the relative pose encoded by the essential matrix, by an end-to-end framework, which is consisted of two novel structures: "-" structure and "|" structure. "-" structure adopts an iterative strategy to learn correspondence features. "|" structure integrates all the features of the iterations and outputs the correspondence weight. In addition, we introduce Permutation-Equivariant Context Squeeze-and-Excitation module, an adapted version of SE module, to process sparse correspondences in a permutation-equivariant way and capture both global and channel-wise contextual information. Extensive experiments on outdoor and indoor scenes show that the proposed T-Net achieves state-of-the-art performance. On outdoor scenes (YFCC100M dataset), T-Net achieves an mAP of 52.28%, a 34.22% precision increase from the best-published result (38.95%). On indoor scenes (SUN3D dataset), T-Net (19.71%) obtains a 21.82% precision increase from the best-published result (16.18%).
Switchable K-Class Hyperplanes for Noise-Robust Representation Learning
Authors: Boxiao Liu, Guanglu Song, Manyuan Zhang, Haihang You, Yu Liu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Model Optimization, Representation Learning, valid

    Optimizing the K-class hyperplanes in the latent space has become the standard paradigm for efficient representation learning. However, it's almost impossible to find an optimal K-class hyperplane to accurately describe the latent space of massive noisy data. For this potential problem, we constructively propose a new method, named Switchable K-class Hyperplanes (SKH), to sufficiently describe the latent space by the mixture of K-class hyperplanes. It can directly replace the conventional single K-class hyperplane optimization as the new paradigm for noise-robust representation learning. When collaborated with the popular ArcFace on million-level data representation learning, we found that the switchable manner in SKH can effectively eliminate the gradient conflict generated by real-world label noise on a single K-class hyperplane. Moreover, combined with the margin-based loss functions (e.g. ArcFace), we propose a simple Posterior Data Clean strategy to reduce the model optimization deviation on clean dataset caused by the reduction of valid categories in each K-class hyperplane. Extensive experiments demonstrate that the proposed SKH easily achieves new state-of-the-art on IJB-B and IJB-C by encouraging noise-robust representation learning.
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval
Authors: Yun Wang, Tong Zhang, Xueya Zhang, Zhen Cui, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jian Yang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Cross-Modal Retrieval, Graph Embedding, Graph Learning, Retrieval

    Graphs play an important role in cross-modal image-text understanding as they characterize the intrinsic structure which is robust and crucial for the measurement of cross-modal similarity. In this work, we propose a Wasserstein Coupled Graph Learning (WCGL) method to deal with the cross-modal retrieval task. First, graphs are constructed according to two input cross-modal samples separately, and passed through the corresponding graph encoders to extract robust features. Then, a Wasserstein coupled dictionary, containing multiple pairs of counterpart graph keys with each key corresponding to one modality, is constructed for further feature learning. Based on this dictionary, the input graphs can be transformed into the dictionary space to facilitate the similarity measurement through a Wasserstein Graph Embedding (WGE) process. The WGE could capture the graph correlation between the input and each corresponding key through optimal transport, and hence well characterize the inter-graph structural relationship. To further achieve discriminant graph learning, we specifically define a Wasserstein discriminant loss on the coupled graph keys to make the intra-class (counterpart) keys more compact and inter-class (non-counterpart) keys more dispersed, which further promotes the final cross-modal retrieval task. Experimental results demonstrate the effectiveness and state-of-the-art performance.
Interacting Two-Hand 3D Pose and Shape Reconstruction From Single Color Image
Authors: Baowen Zhang, Yangang Wang, Xiaoming Deng, yinda zhang, Ping Tan, Cuixia Ma, Hongan Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Interacting Hand Pose Estimation

    In this paper, we propose a novel deep learning framework to reconstruct 3D hand poses and shapes of two interacting hands from a single color image. Previous methods designed for single hand cannot be easily applied for the two hand scenario because of the heavy inter-hand occlusion and larger solution space. In order to address the occlusion and similar appearance between hands that may confuse the network, we design a hand pose-aware attention module to extract features associated to each individual hand respectively. We then leverage the two hand context presented in interaction and propose a context-aware cascaded refinement that improves the hand pose and shape accuracy of each hand conditioned on the context between interacting hands. Extensive experiments on the main benchmark datasets demonstrate that our method predicts accurate 3D hand pose and shape from single color image, and achieves the state-of-the-art performance. Code is available in project webpage https://baowenz.github.io/Intershape/.