pwc-archive/papers-with-abstracts huggingface dataset:
PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization
Authors: Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Representation Learning, Scene Segmentation

    From an early age, humans perceive the visual world as composed of coherent objects with distinctive properties such as shape, size, and color. There is great interest in building models that are able to learn similar structure, ideally in an unsupervised manner. Learning such structure from complex 3D scenes that include clutter, occlusions, interactions, and camera motion is still an open challenge. We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner. Our model (named PARTS) builds on recent approaches that utilize iterative amortized inference and transition dynamics for deep generative models. We achieve dramatic improvements in performance by introducing several novel contributions. We introduce a recurrent slot-attention like encoder which allows for top-down influence during inference. Unlike prior work, we eschew using an auto-regressive prior when modeling image sequences, and demonstrate that a fixed frame-independent prior is superior for the purpose of scene segmentation and representation learning. We demonstrate our model's success on three different video datasets (the popular benchmark CLEVRER; a simulated 3D Playroom environment; and a real-world Robotics Arm dataset). Finally, we analyze the contributions of the various model components and the representations learned by the model.
Adaptive Curriculum Learning
Authors: Yajing Kong, Liu Liu, Jun Wang, DaCheng Tao
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Binary Classification

    Inspired by the human learning principle that learning easier concepts first and then gradually paying more attention to harder ones, curriculum learning uses the non-uniform sampling of mini-batches according to the order of examples' difficulty. Just as a teacher adjusts the curriculum according to the learning progress of each student, a proper curriculum should be adapted to the current state of the model. Therefore, in contrast to recent works using a fixed curriculum, we devise a new curriculum learning method, Adaptive Curriculum Learning (Adaptive CL), adapting the difficulty of examples to the current state of the model. Specifically, we make use of the loss of the current model to adjust the difficulty score while retaining previous useful learned knowledge by KL divergence. Moreover, under a non-linear model and binary classification, we theoretically prove that the expected convergence rate of curriculum learning monotonically decreases with respect to the loss of a point regarding the optimal hypothesis, and monotonically increases with respect to the loss of a point regarding the current hypothesis. The analyses indicate that Adaptive CL could improve the convergence properties during the early stages of learning. Extensive experimental results demonstrate the superiority of the proposed approach over existing competitive curriculum learning methods.
Motion Adaptive Pose Estimation From Compressed Videos
Authors: Zhipeng Fan, Jun Liu, Yao Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Motion Compensation, Pose Estimation

    Human pose estimation from videos has many real-world applications. Existing methods focus on applying models with a uniform computation profile on fully de- coded frames, ignoring the freely available motion signals and motion-compensation residuals from the compressed stream. A novel model, called Motion Adaptive Pose Net is proposed to exploit the compressed streams to efficiently decode pose sequences from videos. The model incorporates a Motion Compensated ConvLSTM to propagate the spatially aligned features, along with an adaptive gate to dynamically determine if the computationally expensive features should be extracted from fully decoded frames to compensate the motion-warped features, solely based on the residual errors. Leveraging the informative yet readily available signals from compressed streams, we propagate the latent features through our Motion Adaptive Pose Net efficiently. Our model outperforms the state-of-the-art models in pose- estimation accuracy on two widely used datasets with only around half of the computation complexity.
Active Universal Domain Adaptation
Authors: Xinhong Ma, Junyu Gao, Changsheng Xu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Active Learning, Domain Adaptation, Universal Domain Adaptation, Unsupervised Domain Adaptation

    Most unsupervised domain adaptation methods rely on rich prior knowledge about the source-target label set relationship, and they cannot recognize categories beyond the source classes, which limits their applicability in practical scenarios. This paper proposes a new paradigm for unsupervised domain adaptation, termed as Active Universal Domain Adaptation (AUDA), which removes all label set assumptions and aims for not only recognizing target samples from source classes but also inferring those from target-private classes by using active learning to annotate a small budget of target data. For AUDA, it is challenging to jointly adapt the model to the target domain and select informative target samples for annotations under a large domain gap and significant semantic shift. To address the problems, we propose an Active Universal Adaptation Network (AUAN). Specifically, we first introduce Adversarial and Diverse Curriculum Learning (ADCL), which progressively aligns source and target domains to classify whether target samples are from source classes. Then, we propose a Clustering Non-transferable Gradient Embedding (CNTGE) strategy, which utilizes the clues of transferability, diversity, and uncertainty to annotate target informative sample, making it possible to infer labels for target samples of target-private classes. Finally, we propose to jointly train ADCL and CNTGE with target supervision to promote domain adaptation and target-private class recognition. Extensive experiments demonstrate that the proposed AUDA model equipped with ADCL and CNTGE achieves significant results on four popular benchmarks.
Neural Image Compression via Attentional Multi-Scale Back Projection and Frequency Decomposition
Authors: Ge Gao, Pei You, Rong pan, Shunyuan Han, Yuanyuan Zhang, Yuchao Dai, Hojae Lee
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Compression, MS-SSIM, SSIM

    In recent years, neural image compression emerges as a rapidly developing topic in computer vision, where the state-of-the-art approaches now exhibit superior compression performance than their conventional counterparts. Despite the great progress, current methods still have limitations in preserving fine spatial details for optimal reconstruction, especially at low compression rates. We make three contributions in tackling this issue. First, we develop a novel back projection method with attentional and multi-scale feature fusion for augmented representation power. Our back projection method recalibrates the current estimation by establishing feedback connections between high-level and low-level attributes in an attentional and discriminative manner. Second, we propose to decompose the input image and separately process the distinct frequency components, whose derived latents are recombined using a novel dual attention module, so that details inside regions of interest could be explicitly manipulated. Third, we propose a novel training scheme for reducing the latent rounding residual. Experimental results show that, when measured in PSNR, our model reduces BD-rate by 9.88% and 10.32% over the state-of-the-art method, and 4.12% and 4.32% over the latest coding standard Versatile Video Coding (VVC) on the Kodak and CLIC2020 Professional Validation dataset, respectively. Our approach also produces more visually pleasant images when optimized for MS-SSIM. The significant improvement upon existing methods shows the effectiveness of our method in preserving and remedying spatial information for enhanced compression quality.
Self-Born Wiring for Neural Trees
Authors: Ying Chen, Feng Mao, Jie Song, Xinchao Wang, Huiqiong Wang, Mingli Song
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Representation Learning

    Neural trees aim at integrating deep neural networks and decision trees so as to bring the best of the two worlds, including representation learning from the former and faster inference from the latter. In this paper, we introduce a novel approach, termed as Self-born Wiring (SeBoW), to learn neural trees from a mother deep neural network. In contrast to prior neural-tree approaches that either adopt a pre-defined structure or grow hierarchical layers in a progressive manner, task-adaptive neural trees in SeBoW evolve from a deep neural network through a construction-by-destruction process, enabling a global-level parameter optimization that further yields favorable results. Specifically, given a designated network configuration like VGG, SeBoW disconnects all the layers and derives isolated filter groups, based on which a global-level wiring process is conducted to attach a subset of filter groups, eventually bearing a lightweight neural tree. Extensive experiments demonstrate that, with a lower computational cost, SeBoW outperforms all prior neural trees by a significant margin and even achieves results on par with predominant non-tree networks like ResNets. Moreover, SeBoW proves its scalability to large-scale datasets like ImageNet, which has been barely explored by prior tree networks.
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition
Authors: Yaobin Zhang, Weihong Deng, Yaoyao Zhong, Jiani Hu, Xian Li, Dongyue Zhao, Dongchao Wen
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Face Recognition, Meta-Learning, Transfer Learning

    The training of a deep face recognition system usually faces the interference of label noise in the training data. However, it is difficult to obtain a high-precision cleaning model to remove these noises. In this paper, we propose an adaptive label noise cleaning algorithm based on meta-learning for face recognition datasets, which can learn the distribution of the data to be cleaned and make automatic adjustments based on class differences. It first learns reliable cleaning knowledge from well-labeled noisy data, then gradually transfers it to the target data with meta-supervision to improve performance. A threshold adapter module is also proposed to address the drift problem in transfer learning methods. Extensive experiments clean two noisy in-the-wild face recognition datasets and show the effectiveness of the proposed method to reach state-of-the-art performance on the IJB-C face recognition benchmark.
Motion Prediction Using Trajectory Cues
Authors: Zhenguang Liu, Pengxiang Su, Shuang Wu, Xuanjing Shen, Haipeng Chen, Yanbin Hao, Meng Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: motion prediction, Prediction

    Predicting human motion from a historical pose sequence is at the core of many applications in computer vision. Current state-of-the-art methods concentrate on learning motion contexts in the pose space, however, the high dimensionality and complex nature of human pose invoke inherent difficulties in extracting such contexts. In this paper, we instead advocate to model motion contexts in the joint trajectory space, as the trajectory of a joint is smooth, vectorial, and gives sufficient information to the model. Moreover, most existing methods consider only the dependencies between skeletal connected joints, disregarding prior knowledge and the hidden connections between geometrically separated joints. Motivated by this, we present a semi-constrained graph to explicitly encode skeletal connections and prior knowledge, while adaptively learn implicit dependencies between joints. We also explore the applications of our approach to a range of objects including human, fish, and mouse. Surprisingly, our method sets the new state-of-the-art performance on 4 different benchmark datasets, a remarkable highlight is that it achieves a 19.1% accuracy improvement over current state-of-the-art in average. To facilitate future research, we have released our code at https://github.com/Pose-Group/MPT.
CryoDRGN2: Ab Initio Neural Reconstruction of 3D Protein Structures From Real Cryo-EM Images
Authors: Ellen D. Zhong, Adam Lerer, Joseph H. Davis, Bonnie Berger
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10

    Protein structure determination from cryo-EM data requires reconstructing a 3D volume (or distribution of volumes) from many noisy and randomly oriented 2D projection images. While the standard homogeneous reconstruction task aims to recover a single static structure, recently-proposed neural and non-neural methods can reconstruct distributions of structures, thereby enabling the study of protein complexes that possess intrinsic structural or conformational heterogeneity. These heterogeneous reconstruction methods, however, require fixed image poses, which are typically estimated from an upstream homogeneous reconstruction and are not guaranteed to be accurate under highly heterogeneous conditions. In this work we describe cryoDRGN2, an ab initio reconstruction algorithm, which can jointly estimate image poses and learn a neural model of a distribution of 3D structures on real heterogeneous cryo-EM data. To achieve this, we adapt search algorithms from the traditional cryo-EM literature, and describe the optimizations and design choices required to make such a search procedure computationally tractable in the neural model setting. We show that cryoDRGN2 is robust to the high noise levels of real cryo-EM images, trains faster than earlier neural methods, and achieves state-of-the-art performance on real cryo-EM datasets.
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation
Authors: Yunhang Shen, Liujuan Cao, Zhiwei Chen, Baochang Zhang, Chi Su, Yongjian Wu, Feiyue Huang, Rongrong Ji
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Self-Supervised Learning, Semantic Segmentation, Weakly-supervised instance segmentation, Weakly Supervised Object Detection

    Weakly supervised instance segmentation (WSIS) with only image-level labels has recently drawn much attention. To date, bottom-up WSIS methods refine discriminative cues from classifiers with sophisticated multi-stage training procedures, which also suffer from inconsistent object boundaries. And top-down WSIS methods are formulated as cascade detection-to-segmentation pipeline, in which the quality of segmentation learning heavily depends on pseudo masks generated from detectors. In this paper, we propose a unified parallel detection-and-segmentation learning (PDSL) framework to learn instance segmentation with only image-level labels, which draws inspiration from both top-down and bottom-up instance segmentation approaches. The detection module is the same as the typical design of any weakly supervised object detection, while the segmentation module leverages self-supervised learning to model class-agnostic foreground extraction, following by self-training to refine class-specific segmentation. We further design instance-activation correlation module to improve the coherence between detection and segmentation branches. Extensive experiments verify that the proposed method outperforms baselines and achieves the state-of-the-art results on PASCAL VOC and MS COCO.
Foreground Activation Maps for Weakly Supervised Object Localization
Authors: Meng Meng, Tianzhu Zhang, Qi Tian, Yongdong Zhang, Feng Wu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Classification, Object, Object Localization, Weakly-Supervised Object Localization

    Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, with only image-level labels, learning object classification models tends to activate object parts and ignore the whole object, while expanding object parts into the whole object may deteriorate classification performance. To alleviate this problem, we propose foreground activation maps (FAM), whose aim is to optimize object localization and classification jointly via an object-aware attention module and a part-aware attention module in a unified model, where the two tasks can complement and enhance each other. To the best of our knowledge, this is the first work that can achieve remarkable performance for both tasks by optimizing them jointly via FAM for WSOL. Besides, the designed two modules can effectively highlight foreground objects for localization and discover discriminative parts for classification. Extensive experiments with four backbones on two standard benchmarks demonstrate that our FAM performs favorably against state-of-the-art WSOL methods.
Event Stream Super-Resolution via Spatiotemporal Constraint Learning
Authors: Siqi Li, Yutong Feng, Yipeng Li, Yu Jiang, Changqing Zou, Yue Gao
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Reconstruction, Philosophy, Super-Resolution

    Event cameras are bio-inspired sensors that respond to brightness changes asynchronously and output in the form of event streams instead of frame-based images. They own outstanding advantages compared with traditional cameras: higher temporal resolution, higher dynamic range, and lower power consumption. However, the spatial resolution of existing event cameras is insufficient and challenging to be enhanced at the hardware level while maintaining the asynchronous philosophy of circuit design. Therefore, it is imperative to explore the algorithm of event stream super-resolution, which is a non-trivial task due to the sparsity and strong spatio-temporal correlation of the events from an event camera. In this paper, we propose an end-to-end framework based on spiking neural network for event stream super-resolution, which can generate high-resolution (HR) event stream from the input low-resolution (LR) event stream. A spatiotemporal constraint learning mechanism is proposed to learn the spatial and temporal distributions of the event stream simultaneously. We validate our method on four large-scale datasets and the results show that our method achieves state-of-the-art performance. The satisfying results on two downstream applications, i.e. object classification and image reconstruction, further demonstrate the usability of our method. To prove the application potential of our method, we deploy it on a mobile platform. The high-quality HR event stream generated by our real-time system demonstrates the effectiveness and efficiency of our method.
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack
Authors: Weiwei Feng, Baoyuan Wu, Tianzhu Zhang, Yong Zhang, Yongdong Zhang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Adversarial Attack, Few-Shot Learning, Meta-Learning

    Modern deep neural networks are often vulnerable to adversarial examples. Most exist attack methods focus on crafting adversarial examples in the digital domain, while only limited works study physical adversarial attack. However, it is more challenging to generate effective adversarial examples in the physical world due to many uncontrollable physical dynamics. Most current physical attack methods aim to generate robust physical adversarial examples by simulating all possible physical dynamics. When attacking new images or new DNN models, they require expensive manually efforts for simulating physical dynamics and considerable time for iteratively optimizing for each image. To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images. To the best of our knowledge, this is the first work to formulate the physical attack as a few-shot learning problem. Here, the training task is redefined as the composition of a support set, a query set, and a target DNN model. Under the few- shot setting, we design a novel class-agnostic and model-agnostic meta-learning algorithm to enhance the generalization ability of our method. Extensive experimental results on two benchmark datasets with four challenging experimental settings verify the superior robustness and generalization of our method by comparing to state-of-the-art physical attack methods.
Real-Time Vanishing Point Detector Integrating Under-Parameterized RANSAC and Hough Transform
Authors: Jianping Wu, Liang Zhang, Ye Liu, Ke Chen
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10

    We propose a novel approach that integrates under-parameterized RANSAC (UPRANSAC) with Hough Transform to detect vanishing points (VPs) from un-calibrated monocular images. In our algorithm, the UPRANSAC chooses one hypothetical inlier in a sample set to find a portion of the VP's degrees of freedom, which is followed by a highly reliable brute-force voting scheme (1-D Hough Transform) to find the VP's remaining degrees of freedom along the extension line of the hypothetical inlier. Our approach is able to sequentially find a series of VPs by repeatedly removing inliers of any detected VPs from minimal sample sets until the stop criterion is reached. Compared to traditional RANSAC that selects 2 edges as a hypothetical inlier pair to fit a model of VP hypothesis and requires hitting a pair of inliners, the UPRANSAC has a higher likelihood to hit one inliner and is more reliable in VP detection. Meanwhile, the tremendously scaled-down voting space with the requirement of only 1 parameter for processing significantly increased the performance efficiency of Hough Transform in our scheme. Testing results with well-known benchmark datasets show that the detection accuracies of our approach were higher or on par with the SOTA while running in deeply real-time zone.
Watch Only Once: An End-to-End Video Action Detection Framework
Authors: Shoufa Chen, Peize Sun, Enze Xie, Chongjian Ge, Jiannan Wu, Lan Ma, Jiajun Shen, Ping Luo
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Classification, Action Detection, Classification, Video Action Detection

    We propose an end-to-end pipeline, named Watch Once Only (WOO), for video action detection. Current methods either decouple video action detection task into separated stages of actor localization and action classification or train two separated models within one stage. In contrast, our approach solves the actor localization and action classification simultaneously in a unified network. The whole pipeline is significantly simplified by unifying the backbone network and eliminating many hand-crafted components. WOO takes a unified video backbone to simultaneously extract features for actor location and action classification. In addition, we introduce spatial-temporal action embeddings into our framework and design a spatial-temporal fusion module to obtain more discriminative features with richer information, which further boosts the action classification performance. Extensive experiments on AVA and JHMDB datasets show that WOO achieves state-of-the-art performance, while still reduces up to 16.7% GFLOPs compared with existing methods. We hope our work can inspire rethinking the convention of action detection and serve as a solid baseline for end-to-end action detection. Code is available.