pwc-archive/papers-with-abstracts huggingface dataset:
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models
Authors: Jie Li, Rongrong Ji, Peixian Chen, Baochang Zhang, Xiaopeng Hong, Ruixin Zhang, Shaoxin Li, Jilin Li, Feiyue Huang, Yongjian Wu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Dimensionality Reduction

    The decision-based black-box attack means to craft adversarial examples with only the top-1 label of the victim model available. A common practice is to start from a large perturbation and then iteratively reduce it with a deterministic direction and a random one while keeping it adversarial. The limited information obtained from each query and inefficient direction sampling impede attack efficiency, making it hard to obtain a small enough perturbation within a limited number of queries. To tackle this problem, we propose a novel attack method termed Adaptive History-driven Attack (AHA) which gathers information from all historical queries as the prior for current sampling. Moreover, to balance between the deterministic direction and the random one, we dynamically adjust the coefficient according to the ratio of the actual magnitude reduction to the expected one. Such a strategy improves the success rate of queries during optimization, letting adversarial examples move swiftly along the decision boundary. Our method can also integrate with subspace optimization like dimension reduction to further improve efficiency. Extensive experiments on both ImageNet and CelebA datasets demonstrate that our method achieves at least 24.3% lower magnitude of perturbation on average with the same number of queries. Finally, we prove the practical potential of our method by evaluating it on popular defense methods and a real-world system provided by MEGVII Face++.
Cortical Surface Shape Analysis Based on Alexandrov Polyhedra
Authors: Min Zhang, Yang Guo, Na lei, Zhou Zhao, Jianfeng Wu, Xiaoyin Xu, Yalin Wang, Xianfeng GU
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Prognosis

    Shape analysis has been playing an important role in early diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's diseases (AD). However, obtaining effective shape representations remains challenging. This paper proposes to use the Alexandrov polyhedra as surface-based shape signatures for cortical morphometry analysis. Given a closed genus-0 surface, its Alexandrov polyhedron is a convex representation that encodes its intrinsic geometry information. We propose to compute the polyhedra via a novel spherical optimal transport (OT) computation. In our experiments, we observe that the Alexandrov polyhedra of cortical surfaces between pathology-confirmed AD and cognitively unimpaired individuals are significantly different. Moreover, we propose a visualization method by comparing local geometry differences across cortical surfaces. We show that the proposed method is effective in pinpointing regional cortical structural changes impacted by AD.
Collaborative Learning With Disentangled Features for Zero-Shot Domain Adaptation
Authors: Won Young Jhoo, Jae-Pil Heo
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation

    Typical domain adaptation techniques aim to transfer label information from a label-rich source domain to a label-scarce target domain in the same label space. However, it is often hard to get even the unlabeled target domain data of a task of interest. In such a case, we can capture the domain shift between the source domain and target domain from an unseen task and transfer it to the task of interest, which is known as zero-shot domain adaptation (ZSDA). Existing state-of-the-art methods for ZSDA attempted to generate target domain data. However, training such generative models causes significant computational overhead and is hardly optimized. In this paper, we propose a novel ZSDA method that learns a task-agnostic domain shift by collaborative training of domain-invariant semantic features and task-invariant domain features via adversarial learning. Meanwhile, the spatial attention map is learned from disentangled feature representations to selectively emphasize the domain-specific salient parts of the domain-invariant features. Experimental results show that our ZSDA method achieves state-of-the-art performance on several benchmarks.
Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation
Authors: Zhenbo Yu, Bingbing Ni, Jingwei Xu, Junjie Wang, Chenglong Zhao, Wenjun Zhang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation, Unsupervised 3D Human Pose Estimation

    In this work, we study the ambiguity problem in the task of unsupervised 3D human pose estimation from 2D counterpart. On one hand, without explicit annotation, the scale of 3D pose is difficult to be accurately captured (scale ambiguity). On the other hand, one 2D pose might correspond to multiple 3D gestures, where the lifting procedure is inherently ambiguous (pose ambiguity). Previous methods generally use temporal constraints (e.g., constant bone length and motion smoothness) to alleviate the above issues. However, these methods commonly enforce the outputs to fulfill multiple training objectives simultaneously, which often lead to sub-optimal results. In contrast to the majority of previous works, we propose to split the whole problem into two sub-tasks, i.e., optimizing 2D input poses via a scale estimation module and then mapping optimized 2D pose to 3D counterpart via a pose lifting module. Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively. These two modules are optimized via a iterative training scheme with corresponding temporal constraints, which effectively reduce the learning difficulty and lead to better performance. Results on the Human3.6M dataset demonstrate that our approach improves upon the prior art by 23.1% and also outperforms several weakly supervised approaches that rely on 3D annotations. Our project is available at https://sites.google.com/view/ambiguity-aware-hpe.
Robust Small Object Detection on the Water Surface Through Fusion of Camera and Millimeter Wave Radar
Authors: Yuwei Cheng, Hu Xu, Yimin Liu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Autonomous Navigation, Object, object-detection, Object Detection, Small Object Detection

    In recent years, unmanned surface vehicles (USVs) have been experiencing growth in various applications. With the expansion of USVs' application scenes from the typical marine areas to inland waters, new challenges arise for the object detection task, which is an essential part of the perception system of USVs. In our work, we focus on a relatively unexplored task for USVs in inland waters: small object detection on water surfaces, which is of vital importance for safe autonomous navigation and USVs' certain missions such as floating waste cleaning. Considering the limitations of vision-based object detection, we propose a novel vision-radar fusion based method for robust small object detection on water surfaces. By using a novel representation format of millimeter wave radar point clouds and applying a deep-level multi-scale fusion of RGB images and radar data, the proposed method can efficiently utilize the characteristics of radar data and improve the accuracy and robustness for small object detection on water surfaces. We test the method on the real-world floating bottle dataset that we collected and released. The result shows that, our method improves the average detection accuracy significantly compared to the vision-based methods and achieves state-of-the-art performance. Besides, the proposed method performs robustly when single sensor degrades.
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
Authors: Shu Yang, Lu Zhang, Jinqing Qi, Huchuan Lu, Shuo Wang, Xiaoxing Zhang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation, Zero-Shot Video Object Segmentation

    How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation. In this paper, we propose an Attentive Multi-Modality Collaboration Network (AMC-Net) to utilize appearance and motion information uniformly. Specifically, AMC-Net fuses robust information from multi-modality features and promotes their collaboration in two stages. First, we propose a Multi-Modality Co-Attention Gate (MCG) on the bilateral encoder branches, in which a gate function is used to formulate co-attention scores for balancing the contributions of multi-modality features and suppressing the redundant and misleading information. Then, we propose a Motion Correction Module (MCM) with a visual-motion attention mechanism, which is constructed to emphasize the features of foreground objects by incorporating the spatio-temporal correspondence between appearance and motion cues. Extensive experiments on three public challenging benchmark datasets verify that our proposed network performs favorably against existing state-of-the-art methods via training with fewer data.
Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds
Authors: Longkun Zou, Hui Tang, Ke Chen, Kui Jia
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Point Cloud Classification, Representation Learning, Unsupervised Domain Adaptation

    The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets. To improve discrimination on unseen distribution of point-based geometries in a practical and feasible perspective, this paper proposes a new method of geometry-aware self-training (GAST) for unsupervised domain adaptation of object point cloud classification. Specifically, this paper aims to learn a domain-shared representation of semantic categories, via two novel self-supervised geometric learning tasks as feature regularization. On one hand, the representation learning is empowered by a linear mixup of point cloud samples with their self-generated rotation labels, to capture a global topological configuration of local geometries. On the other hand, a diverse point distribution across datasets can be normalized with a novel curvature-aware distortion localization. Experiments on the PointDA-10 dataset show that our GAST method can significantly outperform the state-of-the-art methods.
Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning
Authors: Ming-Xian Lin, Jie Yang, He Wang, Yu-Kun Lai, Rongfei Jia, Binqiang Zhao, Lin Gao
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Shape Retrieval, Contrastive Learning, Data Augmentation, Metric Learning, Representation Learning, Retrieval, Triplet

    In this work, we tackle the problem of single image-based 3D shape retrieval (IBSR), where we seek to find the most matched shape of a given single 2D image from a shape repository. Most of the existing works learn to embed 2D images and 3D shapes into a common feature space and perform metric learning using a triplet loss. Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning. We note that adopting such cross-modal contrastive learning between 2D images and 3D shapes into IBSR tasks is non-trivial and challenging: contrastive learning requires very strong data augmentation in constructed positive pairs to learn the feature invariance, whereas traditional metric learning works do not have this requirement. Moreover, object shape and appearance are entangled in 2D query images, thus making the learning task more difficult than contrasting single-modal data. To mitigate the challenges, we propose to use multi-view grayscale rendered images from the 3D shapes as a shape representation. We then introduce a strong data augmentation technique based on color transfer, which can significantly but naturally change the appearance of the query image, effectively satisfying the need for contrastive learning. Finally, we propose to incorporate a novel category-level contrastive loss that helps distinguish similar objects from different categories, in addition to classic instance-level contrastive loss. Our experiments demonstrate that our approach achieves the best performance on all the three popular IBSR benchmarks, including Pix3D, Stanford Cars, and Comp Cars, outperforming the previous state-of-the-art from 4% - 15% on retrieval accuracy.
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge Distillation
Authors: Li Liu, Qingle Huang, Sihao Lin, Hongwei Xie, Bing Wang, Xiaojun Chang, Xiaodan Liang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Diversity, Knowledge Distillation

    Knowledge Distillation has shown very promising ability in transferring learned representation from the larger model (teacher) to the smaller one (student). Despite many efforts, prior methods ignore the important role of retaining inter-channel correlation of features, leading to the lack of capturing intrinsic distribution of the feature space and sufficient diversity properties of features in the teacher network. To solve the issue, we propose the novel Inter-Channel Correlation for Knowledge Distillation (ICKD), with which the diversity and homology of the feature space of the student network can align with that of the teacher network. The correlation between these two channels is interpreted as diversity if they are irrelevant to each other, otherwise homology. Then the student is required to mimic the correlation within its own embedding space. In addition, we introduce the grid-level inter-channel correlation, making it capable of dense prediction tasks. Extensive experiments on two vision tasks, including ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consistently outperforms many existing methods, advancing the state-of-the-art in the fields of Knowledge Distillation. To our knowledge, we are the first method based on knowledge distillation boosts ResNet18 beyond 72% Top-1 accuracy on ImageNet classification. Code is available at: https://github.com/ADLab-AutoDrive/ICKD.
iNAS: Integral NAS for Device-Aware Salient Object Detection
Authors: Yu-Chao Gu, Shang-Hua Gao, Xu-Sheng Cao, Peng Du, Shao-Ping Lu, Ming-Ming Cheng
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: CPU, Neural Architecture Search, Object, object-detection, Object Detection, Salient Object Detection

    Existing salient object detection (SOD) models usually focus on either backbone feature extractors or saliency heads, ignoring their relations. A powerful backbone could still achieve sub-optimal performance with a weak saliency head and vice versa. Moreover, the balance between model performance and inference latency poses a great challenge to model design, especially when considering different deployment scenarios. Considering all components in an integral neural architecture search (iNAS) space, we propose a flexible device-aware search scheme that only trains the SOD model once and quickly finds high-performance but low-latency models on multiple devices. An evolution search with latency-group sampling (LGS) is proposed to explore the entire latency area of our enlarged search space. Models searched by iNAS achieve similar performance with SOTA methods but reduce the 3.8x, 3.3x, 2.6x, 1.9x latency on Huawei Nova6 SE, Intel Core CPU, the Jetson Nano, and Nvidia Titan Xp. The code is released at https://mmcheng.net/inas/.
High Quality Disparity Remapping With Two-Stage Warping
Authors: Bing Li, Chia-Wen Lin, Cheng Zheng, Shan Liu, Junsong Yuan, Bernard Ghanem, C.-C. Jay Kuo
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Vocal Bursts Intensity Prediction, Vocal Bursts Valence Prediction

    A high quality disparity remapping method that preserves 2D shapes and 3D structures, and adjusts disparities of important objects in stereo image pairs is proposed. It is formulated as a constrained optimization problem, whose solution is challenging, since we need to meet multiple requirements of disparity remapping simultaneously. The one-stage optimization process either degrades the quality of important objects or introduces serious distortions in background regions. To address this challenge, we propose a two-stage warping process to solve it. In the first stage, we develop a warping model that finds the optimal warping grids for important objects to fulfill multiple requirements of disparity remapping. In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure. The superior performance of the proposed method is demonstrated by experimental results
Co2L: Contrastive Continual Learning
Authors: Hyuntak Cha, Jaeho Lee, Jinwoo Shin
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Continual Learning, Contrastive Learning, image-classification, Image Classification, Self-Supervised Learning

    Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks than cross-entropy based methods which rely on task-specific supervision. In this paper, we found that the similar holds in the continual learning context: contrastively learned representations are more robust against the catastrophic forgetting than ones trained with the cross-entropy objective. Based on this novel observation, we propose a rehearsal-based continual learning algorithm that focuses on continually learning and maintaining transferable representations. More specifically, the proposed scheme (1) learns representations using the contrastive learning objective, and (2) preserves learned representations using a self-supervised distillation step. We conduct extensive experimental validations under popular benchmark image classification datasets, where our method sets the new state-of-the-art performance. Source code is available at https://github.com/chaht01/Co2L.
SIGNET: Efficient Neural Representation for Light Fields
Authors: Brandon Yushan Feng, Amitabh Varshney
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Super-Resolution

    We present a novel neural representation for light field content that enables compact storage and easy local reconstruction with high fidelity. We use a fully-connected neural network to learn the mapping function between each light field pixel's coordinates and its corresponding color values. However, neural networks that simply take in raw coordinates are unable to accurately learn data containing fine details. We present an input transformation strategy based on the Gegenbauer polynomials which previously showed theoretical advantages over the Fourier basis. We conduct experiments that show our Gegenbauer-based design combined with sinusoidal activation functions leads to a better light field reconstruction quality than a variety of network designs, including those with Fourier-inspired techniques introduced by prior works. Moreover, our SInusoidal Gegenbauer NETwork, or SIGNET, can represent light field scenes more compactly than the state-of-the-art compression methods while maintaining a comparable reconstruction quality. SIGNET also innately allows random access to encoded light field pixels due to its functional design. Furthermore, we demonstrate that SIGNET facilitates super-resolution along the spatial, angular, and temporal dimensions of a light field without any additional training.
Extreme Structure From Motion for Indoor Panoramas Without Visual Overlaps
Authors: Mohammad Amin Shabani, Weilian Song, Makoto Odamaki, Hirochika Fujiki, Yasutaka Furukawa
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10

    This paper proposes an extreme structure from motion (SfM) algorithm for residential indoor panoramas that have little to no visual overlaps. Only a single panorama is present in a room for many cases, making the task infeasible for existing SfM algorithms. Our idea is to learn to evaluate the realism of room/door/window arrangements in the top-down semantic space. After using heuristics to enumerate possible arrangements based on door detections, we evaluate their realism scores, pick the most realistic arrangement, and return the corresponding camera poses. We evaluate the proposed approach on a dataset of 1029 panorama images with 286 houses. Our qualitative and quantitative evaluations show that an existing SfM approach completely fails for most of the houses. The proposed approach achieves the mean positional error of less than 1.0 meter for 47% of the houses and even 78% when considering the top five reconstructions. We will share the code and data in https://github.com/aminshabani/extreme-indoor-sfm.
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution
Authors: Salma Abdel Magid, Yulun Zhang, Donglai Wei, Won-Dong Jang, Zudi Lin, Yun Fu, Hanspeter Pfister
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Super-Resolution, Super-Resolution

    Deep convolutional neural networks (CNNs) have pushed forward the frontier of super-resolution (SR) research. However, current CNN models exhibit a major flaw: they are biased towards learning low-frequency signals. This bias becomes more problematic for the image SR task which targets reconstructing all fine details and image textures. To tackle this challenge, we propose to improve the learning of high-frequency features both locally and globally and introduce two novel architectural units to existing SR models. Specifically, we propose a dynamic high-pass filtering (HPF) module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals. We also propose a matrix multi-spectral channel attention (MMCA) module that predicts the attention map of features decomposed in the frequency domain. This module operates in a global context to adaptively recalibrate feature responses at different frequencies. Extensive qualitative and quantitative results demonstrate that our proposed modules achieve better accuracy and visual improvements against state-of-the-art methods on several benchmark datasets.
One-Pass Multi-View Clustering for Large-Scale Data
Authors: Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Li Liu, Siqi Wang, Weixuan Liang, Jiangyong Shi
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Clustering

    Existing non-negative matrix factorization based multi-view clustering algorithms compute multiple coefficient matrices respect to different data views, and learn a common consensus concurrently. The final partition is always obtained from the consensus with classical clustering techniques, such as k-means. However, the non-negativity constraint prevents from obtaining a more discriminative embedding. Meanwhile, this two-step procedure fails to unify multi-view matrix factorization with partition generation closely, resulting in unpromising performance. Therefore, we propose an one-pass multi-view clustering algorithm by removing the non-negativity constraint and jointly optimize the aforementioned two steps. In this way, the generated partition can guide multi-view matrix factorization to produce more purposive coefficient matrix which, as a feedback, improves the quality of partition. To solve the resultant optimization problem, we design an alternate strategy which is guaranteed to be convergent theoretically. Moreover, the proposed algorithm is free of parameter and of linear complexity, making it practical in applications. In addition, the proposed algorithm is compared with recent advances in literature on benchmarks, demonstrating its effectiveness, superiority and efficiency.