pwc-archive/papers-with-abstracts huggingface dataset:
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition
Authors: Lei Fan, Peixi Xiong, Wei Wei, Ying Wu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Knowledge Distillation, Lifelong learning, Scene Recognition

    Intelligent agents with visual sensors are allowed to actively explore their observations for better recognition performance. This task is referred to as Active Recognition (AR). Currently, most methods toward AR are implemented under a fixed-category setting, which constrains their applicability in realistic scenarios that need to incrementally learn new classes without retraining from scratch. Further, collecting massive data for novel categories is expensive. To address this demand, in this paper, we propose a unified framework towards Few-sample Lifelong Active Recognition (FLAR), which aims at performing active recognition on progressively arising novel categories that only have few training samples. Three difficulties emerge with FLAR: the lifelong recognition policy learning, the knowledge preservation of old categories, and the lack of training samples. To this end, our approach integrates prototypes, a robust representation for limited training samples, into a reinforcement learning solution, which motivates the agent to move towards views resulting in more discriminative features. Catastrophic forgetting during lifelong learning is then alleviated with knowledge distillation. Extensive experiments across two datasets, respectively for object and scene recognition, demonstrate that even without large training samples, the proposed approach could learn to actively recognize novel categories in a class-incremental behavior.
Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-Supervised Polyp Segmentation
Authors: Huisi Wu, Guilian Chen, Zhenkun Wen, Jing Qin
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Segmentation, Semantic Segmentation

    Automatic polyp segmentation from colonoscopy images is an essential step in computer aided diagnosis for colorectal cancer. Most of polyp segmentation methods reported in recent years are based on fully supervised deep learning. However, annotation for polyp images by physicians during the diagnosis is time-consuming and costly. In this paper, we present a novel semi-supervised polyp segmentation via collaborative and adversarial learning of focused and dispersive representations learning model, where focused and dispersive extraction module are used to deal with the diversity of location and shape of polyps. In addition, confidence maps produced by a discriminator in an adversarial training framework shows the effectiveness of leveraging unlabeled data and improving the performance of segmentation network. Consistent regularization is further employed to optimize the segmentation networks to strengthen the representation of the outputs of focused and dispersive extraction module. We also propose an auxiliary adversarial learning method to better leverage unlabeled examples to further improve semantic segmentation accuracy. We conduct extensive experiments on two famous polyp datasets: Kvasir-SEG and CVC-Clinic DB. Experimental results demonstrate the effectiveness of the proposed model, consistently outperforming state-of-the-art semi-supervised segmentation models based on adversarial training and even some advanced fully supervised models. Codes will be released upon publication.
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition
Authors: Rasha Friji, Hassen Drira, Faten Chaieb, Hamza Kchok, Sebastian Kurtek
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Recognition, Deep Learning, Skeleton Based Action Recognition, Temporal Action Localization

    Deep Learning architectures, albeit successful in mostcomputer vision tasks, were designed for data with an un-derlying Euclidean structure, which is not usually fulfilledsince pre-processed data may lie on a non-linear space.In this paper, we propose a geometry aware deep learn-ing approach using rigid and non rigid transformation opti-mization for skeleton-based action recognition. Skeleton se-quences are first modeled as trajectories on Kendall's shapespace and then mapped to the linear tangent space. The re-sulting structured data are then fed to a deep learning archi-tecture, which includes a layer that optimizes over rigid andnon rigid transformations of the 3D skeletons, followed bya CNN-LSTM network. The assessment on two large scaleskeleton datasets, namely NTU-RGB+D and NTU-RGB+D120, has proven that the proposed approach outperformsexisting geometric deep learning methods and exceeds re-cently published approaches with respect to the majority of configurations.
Contrast and Order Representations for Video Self-Supervised Learning
Authors: Kai Hu, Jie Shao, YuAn Liu, Bhiksha Raj, Marios Savvides, Zhiqiang Shen
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Recognition, Self-Supervised Action Recognition Linear, Self-Supervised Learning

    This paper studies the problem of learning self-supervised representations on videos. In contrast to image modality that only requires appearance information on objects or scenes, video needs to further explore the relations between multiple frames/clips along the temporal dimension. However, the recent proposed contrastive-based self-supervised frameworks do not grasp such relations explicitly since they simply utilize two augmented clips from the same video and compare their distance without referring to their temporal relation. To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames. In particular, given two video clips, our model first predicts whether they come from the same input video, and then predict the temporal ordering of the clips if they come from the same video. We also propose a novel decoupling attention method to learn symmetric similarity (contrast) and anti-symmetric patterns (order). Such design involves neither extra parameters nor computation, but can speed up the learning process and improve accuracy compared to the vanilla multi-head attention. We extensively validate the representation ability of our learned video features for the downstream action recognition task on Kinetics-400 and Something-something V2. Our method outperforms previous state-of-the-arts by a significant margin.
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective
Authors: Wei Wang, Haochen Zhang, Zehuan Yuan, Changhu Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Image-to-Image Translation, Super-Resolution

    Most existing convolution neural network (CNN) based super-resolution (SR) methods generate their paired training dataset by artificially synthesizing low-resolution (LR) images from the high-resolution (HR) ones. However, this dataset preparation strategy harms the application of these CNNs in real-world scenarios due to the inherent domain gap between the training and testing data. A popular attempts towards the challenge is unpaired generative adversarial networks, which generate "real" LR counterparts from real HR images using image-to-image translation and then perform super-resolution from "real" LR->SR. Despite great progress, it is still difficult to synthesize perfect "real" LR images for super-resolution. In this paper, we firstly consider the real-world SR problem from the traditional domain adaptation perspective. We propose a novel unpaired SR training framework based on feature distribution alignment, with which we can obtain degradation-indistinguishable feature maps and then map them to HR images. In order to generate better SR images for target LR domain, we introduce several regularization losses to force the aligned feature to locate around the target domain. Our experiments indicate that our SR network obtains the state-of-the-art performance over both blind and unpaired SR methods on diverse datasets.
Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation
Authors: Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Chaoliang Zhong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

    The latest heuristic for handling the domain shift in unsupervised domain adaptation tasks is to reduce the data distribution discrepancy using adversarial learning. Recent studies improve the conventional adversarial domain adaptation methods with discriminative information by integrating the classifier's outputs into distribution divergence measurement. However, they still suffer from the equilibrium problem of adversarial learning in which even if the discriminator is fully confused, sufficient similarity between two distributions cannot be guaranteed. To overcome this problem, we propose a novel approach named feature gradient distribution alignment (FGDA). We demonstrate the rationale of our method both theoretically and empirically. In particular, we show that the distribution discrepancy can be reduced by constraining feature gradients of two domains to have similar distributions. Meanwhile, our method enjoys a theoretical guarantee that a tighter error upper bound for target samples can be obtained than that of conventional adversarial domain adaptation methods. By integrating the proposed method with existing adversarial domain adaptation models, we achieve state-of-the-art performance on two real-world benchmark datasets.
Attack-Guided Perceptual Data Generation for Real-World Re-Identification
Authors: Yukun Huang, Xueyang Fu, Zheng-Jun Zha
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Person Re-Identification, Representation Learning

    In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e.g., cross-resolution and insufficient lighting. Due to the limited variation range of training data, existing models are difficult to generalize to scenes with unknown perceptual interference types. To address the above problem, in this paper, we propose two disjoint data-generation ways to complement existing training samples to improve the robustness of Re-ID models. Firstly, considering the sparsity and imbalance of samples in the perceptual space, a dense resampling method from the estimated perceptual distribution is performed. Secondly, to dig more representative generated samples for identity representation learning, we introduce a graph-based white-box attacker to guide the data generation process with intra-batch ranking and discriminate attention. In addition, two synthetic-to-real feature constraints are introduced into the Re-ID training to prevent the generated data from bringing domain bias. Our method is effective, easy-to-implement, and independent of the specific network architecture. Applying our approach to a ResNet-50 baseline can already achieve competitive results, surpassing state-of-the-art methods by +1.2% at Rank-1 on the MLR-CUHK03 dataset.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Authors: Yujun Cai, Yiwei Wang, Yiheng Zhu, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Chuanxia Zheng, Sijie Yan, Henghui Ding, Xiaohui Shen, Ding Liu, Nadia Magnenat Thalmann
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Decoder, motion prediction, Motion Synthesis

    We present a unified and flexible framework to address the generalized problem of 3D motion synthesis that covers the tasks of motion prediction, completion, interpolation, and spatial-temporal recovery. Since these tasks have different input constraints and various fidelity and diversity requirements, most existing approaches only cater to a specific task or use different architectures to address various tasks. Here we propose a unified framework based on Conditional Variational Auto-Encoder (CVAE), where we treat any arbitrary input as a masked motion series. Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series. To further allow the flexibility of manipulating the motion style of the generated series, we design an Action-Adaptive Modulation (AAM) to propagate the given semantic guidance through the whole sequence. We also introduce a cross-attention mechanism to exploit distant relations among decoder and encoder features for better realism and global consistency. We conducted extensive experiments on Human 3.6M and CMU-Mocap. The results show that our method produces coherent and realistic results for various motion synthesis tasks, with the synthesized motions distinctly adapted by the given action labels.
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection
Authors: Hanxue Liang, Chenhan Jiang, Dapeng Feng, Xin Chen, Hang Xu, Xiaodan Liang, Wei zhang, Zhenguo Li, Luc van Gool
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Object Detection, Clustering, Object, object-detection, Object Detection, Representation Learning, Self-Supervised Learning

    Current 3D object detection paradigms highly rely on extensive annotation efforts, which makes them not practical in many real-world industrial applications. Inspired by that a human driver can keep accumulating experiences from self-exploring the roads without any tutor's guidance, we first step forwards to explore a simple yet effective self-supervised learning framework tailored for LiDAR-based 3D object detection. Although the self-supervised pipeline has achieved great success in 2D domain, the characteristic challenges (e.g., complex geometry structure and various 3D object views) encountered in the 3D domain hinder the direct adoption of existing techniques that often contrast the 2D augmented data or cluster single-view features. Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D. First, GCC-3D introduces a Geometric-Aware Contrastive objective to learn spatial-sensitive local structure representation. This objective enforces the spatially-closed voxels to have high feature similarity. Second, a Pseudo-Instance Clustering harmonization mechanism is proposed to encourage that different views of pseudo-instances should have consistent similarities to clustering prototype centers. This module endows our model semantic discriminative capacity. Extensive experiments demonstrate our GCC-3D achieves significant performance improvement on data-efficient 3D object detection benchmarks (nuScenes and Waymo). Moreover, our GCC-3D framework can achieve state-of-the art performances on all popular 3D object detection benchmarks.
Interpretable Image Recognition by Constructing Transparent Embedding Space
Authors: Jiaqi Wang, Huafeng Liu, Xinyue Wang, Liping Jing
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10

    Humans usually explain their reasoning (e.g. classification) by dissecting the image and pointing out the evidence from these parts to the concepts in their minds. Inspired by this cognitive process, several part-level interpretable neural network architectures have been proposed to explain the predictions. However, they suffer from the complex data structure and confusing the effect of the individual part to output category. In this work, an interpretable image recognition deep network is designed by introducing a plug-in transparent embedding space (TesNet) to bridge the high-level input patches (e.g. CNN feature maps) and the output categories. This plug-in embedding space is spanned by transparent basis concepts which are constructed on the Grassmann manifold. These basis concepts are enforced to be category-aware and within-category concepts are orthogonal to each other, which makes sure the embedding space is disentangled. Meanwhile, each basis concept can be traced back to the particular image patches, thus they are transparent and friendly to explain the reasoning process. By comparing with state-of-the-art interpretable methods, TesNet is much more beneficial to classification tasks, esp. providing better interpretability on predictions and improve the final accuracy.
Where Are You Heading? Dynamic Trajectory Prediction With Expert Goal Examples
Authors: He Zhao, Richard P. Wildes
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Trajectory Prediction

    Goal-conditioned approaches recently have been found very useful to human trajectory prediction, when adequate goal estimates are provided. Yet, goal inference is difficult in itself and often incurs extra learning efforts. We propose to predict pedestrian trajectories via the guidance of goal expertise, which can be obtained with modest expense through a novel goal-search mechanism on already seen training examples. There are three key contributions in our study. First, we devise a framework that exploits the nearest examples for high-quality goal position inquiry. This approach naturally considers multi-modality, physical constraints, compatibility with existing methods and is model-free; it therefore does not require additional learning efforts typical in goal inference. Second, we present an end-to-end trajectory predictor that can efficiently associate goal retrievals to past motion information and dynamically infer possible future trajectories. Third, with these two novel techniques in hand, we conduct a series of experiments on two broadly explored datasets (SDD and ETH/UCY) and show that our approach surpasses previous state-of-the-art performance by notable margins and reduces the need for additional parameters.
Dynamic Cross Feature Fusion for Remote Sensing Pansharpening
Authors: Xiao Wu, Ting-Zhu Huang, Liang-Jian Deng, Tian-Jing Zhang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Pansharpening

    Deep Convolution Neural Networks have been adopted for pansharpening and achieved state-of-the-art performance. However, most of the existing works mainly focus on single-scale feature fusion, which leads to failure in fully considering relationships of information between high-level semantics and low-level features, despite the network is deep enough. In this paper, we propose a dynamic cross feature fusion network (DCFNet) for pansharpening. Specifically, DCFNet contains multiple parallel branches, including a high-resolution branch served as the backbone, and the low-resolution branches progressively supplemented into the backbone. Thus our DCFNet can represent the overall information well. In order to enhance the relationships of inter-branches, dynamic cross feature transfers are embedded into multiple branches to obtain high-resolution representations. Then contextualized features will be learned to improve the fusion of information. Experimental results indicate that DCFNet significantly outperforms the prior arts in both quantitative indicators and visual qualities.
Diverse Image Style Transfer via Invertible Cross-Space Mapping
Authors: Haibo Chen, Lei Zhao, Huiming Zhang, Zhizhong Wang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Disentanglement, Diversity, Style Transfer

    Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images. Although style transfer is inherently an underdetermined problem, existing approaches usually assume a deterministic solution, thus failing to capture the full distribution of possible outputs. To address this limitation, we propose a Diverse Image Style Transfer (DIST) framework which achieves significant diversity by enforcing an invertible cross-space mapping. Specifically, the framework consists of three branches: disentanglement branch, inverse branch, and stylization branch. Among them, the disentanglement branch factorizes artworks into content space and style space; the inverse branch encourages the invertible mapping between the latent space of input noise vectors and the style space of generated artistic images; the stylization branch renders the input content image with the style of an artist. Armed with these three branches, our approach is able to synthesize significantly diverse stylized images without loss of quality. We conduct extensive experiments and comparisons to evaluate our approach qualitatively and quantitatively. The experimental results demonstrate the effectiveness of our method.
Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations
Authors: Dat Huynh, Ehsan Elhamifar
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Human-Object Interaction Detection, Multi-label zero-shot learning, Object, Zero-Shot Learning

    We study the problem of multi-label zero-shot recognition in which labels are in the form of human-object interactions (combinations of actions on objects), each image may contain multiple interactions and some interactions do not have training images. We propose a novel compositional learning framework that decouples interaction labels into separate action and object scores that incorporate the spatial compatibility between the two components. We combine these scores to efficiently recognize seen and unseen interactions. However, learning action-object spatial relations, in principle, requires bounding-box annotations, which are costly to gather. Moreover, it is not clear how to generalize spatial relations to unseen interactions. We address these challenges by developing a cross-attention mechanism that localizes objects from action locations and vice versa by predicting displacements between them, referred to as relational directions. During training, we estimate the relational directions as ones maximizing the scores of ground-truth interactions that guide predictions toward compatible action-object regions. By extensive experiments, we show the effectiveness of our framework, where we improve the state of the art by 2.6% mAP score and 5.8% recall score on HICO and Visual Genome datasets, respectively. Code is available at https://github.com/hbdat/iccv21_relational_direction.
A Machine Teaching Framework for Scalable Recognition
Authors: Pei Wang, Nuno Vasconcelos
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: counterfactual, Self-Supervised Learning

    We consider the scalable recognition problem in the fine-grained expert domain where large-scale data collection is easy whereas annotation is difficult. Existing solutions are typically based on semi-supervised or self-supervised learning. We propose an alternative new framework, MEMORABLE, based on machine teaching and online crowdsourcing platforms. A small amount of data is first labeled by experts and then used to teach online annotators for the classes of interest, who finally label the entire dataset. Preliminary studies show that the accuracy of classifiers trained on the final dataset is a function of the accuracy of the student annotators. A new machine teaching algorithm, CMaxGrad, is then proposed to enhance this accuracy by introducing explanations in a state-of-the-art machine teaching algorithm. For this, CMaxGrad leverages counterfactual explanations, which take into account student predictions, thereby proving feedback that is student-specific, explicitly addresses the causes of student confusion, and adapts to the level of competence of the student. Experiments show that both MEMORABLE and CMaxGrad outperform existing solutions to their respective problems.