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MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions

MotionRNN: A Flexible Model for Video Prediction with Spacet...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Haixu Yao, Zhiyu Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly changing across both space and time. Prior methods mainly capture the temporal state transitions but overlook the complex spatiotemporal variations of the motion itself, making them difficult to adapt to ever-changing motions. We observe that physical world motions can be decomposed into transient variation and motion trend, while the latter can be regarded as the accumulation of previous motions. Thus, simultaneously capturing the transient variation and the motion trend is the key to make spacetime-varying motions more predictable. Based on these observations, we propose the MotionRNN framework, which can capture the complex variations within motions and adapt to spacetime-varying scenarios. MotionRNN has two main contributions. The first is that we design the MotionGRU unit, which can model the transient variation and motion trend in a unified way. The second is that we apply the MotionGRU to RNN-based predictive models and indicate a new flexible video prediction architecture with a Motion Highway, which can significantly improve the ability to predict changeable motions and avoid motion vanishing for stacked multiple-layer predictive models. With high flexibility, this framework can adapt to a series of models for deterministic spatiotemporal prediction. Our MotionRNN can yield significant improvements on three challenging benchmarks for video prediction with spacetime-varying motions.

关键词： Road transportation Adaptation models computer vision computer architecture Predictive models Market research Spatiotemporal phenomena

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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pidaparthy, Hemanth Dowling, Michael H. Elder, James H. York Univ N York ON Canada Queens Univ Kingston ON Canada

ISBN: (纸本)9781665448994

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.

关键词： Visualization Fans computer vision conferences Hidden Markov models Games Cameras

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Adversarial Robustness under Long-Tailed Distribution

Adversarial Robustness under Long-Tailed Distribution

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Tong Liu, Ziwei Huang, Qingqiu Wang, Yu Lin, Dahua Chinese Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Huawei Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China SenseTime CUHK Joint Lab Hong Kong Peoples R China Ctr Perceptual & Interact Intelligence Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However;existing works on adversarial robustness mainly focus on balanced datasets, while real-world data usually exhibits a long-tailed distribution. To push adversarial robustness towards more realistic scenarios, in this work we investigate the adversarial vulnerability as well as defense under long-tailed distributions. In particular, we first reveal the negative impacts induced by imbalanced data on both recognition performance and adversarial robustness, uncovering the intrinsic challenges of this problem. We then perform a systematic study on existing long-tailed recognition methods in conjunction with the adversarial training framework. Several valuable observations are obtained: 1) natural accuracy is relatively easy to improve, 2) fake gain of robust accuracy exists under unreliable evaluation, and 3) boundary error limits the promotion of robustness. Inspired by these observations, we propose a clean yet effective framework, RoBal, which consists of two dedicated modules, a scale-invariant classifier and data re-balancing via both margin engineering at training stage and boundary adjustment during inference. Extensive experiments demonstrate the superiority of our approach over other state-of-the-art defense methods. To our best knowledge, we are the first to tackle adversarial robustness under long-tailed distributions, which we believe would be a significant step towards real-world robustness. Our code is available at: https://github. com/wutong16/Adversarial_Long-Tai1.

关键词： Training computer vision Systematics Codes Robustness pattern recognition

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Activity-Biometrics: Person Identification from Daily Activities

Activity-Biometrics: Person Identification from Daily Activi...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Shehreen Azad Yogesh Singh Rawat Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this work, we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of ap-pearance biases such as clothing color and background. We propose ABNet, a novel framework which leverages dis-entanglement of biometric and non-biometric features to perform effective person identification from daily activities. ABNet relies on a bias-less teacher to learn biometric features from RGB videos and explicitly disentangle non-biometric features with the help of biometric distortion. In addition, ABNet also exploits activity prior for biometrics which is enabled by joint biometric and activity learning. We perform comprehensive evaluation of the proposed approach across five different datasets which are derived from existing activity recognition benchmarks. Furthermore, we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets. The code and dataset can be accessed at: https: //***/ sacrcv/Activity-Biometrics/

关键词： Biometrics computer vision Accuracy Biological system modeling Benchmark testing Activity recognition Distortion

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ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring

ARVo: Learning All-Range Volumetric Correspondence for Video...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Dongxu Xu, Chenchen Zhang, Kaihao Yu, Xin Zhong, Yiran Ren, Wenqi Suominen, Hanna Li, Hongdong Australian Natl Univ Canberra ACT Australia DATA61 CSIRO Sydney NSW Australia UTS Sydney NSW Australia Chinese Acad Sci IIE Beijing Peoples R China Univ Turku Turku Finland

ISBN: (纸本)9781665445092

Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neighboring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring.

关键词： Training Solid modeling computer vision Correlation DVD Computational modeling Cameras

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Not just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction

Not just Compete, but Collaborate: Local Image-to-Image Tran...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Daejin Khan, Mohammad Azam Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea Dhaka Power Distribut Co Ltd Dhaka Bangladesh

ISBN: (纸本)9781665445092

Facial attribute editing aims to manipulate the image with the desired attribute while preserving the other details. Recently, generative adversarial networks along with the encoder-decoder architecture have been utilized for this task owing to their ability to create realistic images. However, the existing methods for the unpaired dataset cannot still preserve the attribute-irrelevant regions properly due to the absence of the ground truth image. This work proposes a novel, intuitive loss function called the CAM-consistency loss, which improves the consistency of an input image in image translation. While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator. Our CAM-consistency loss directly optimizes such a Grad-CAM output from the discriminator during training, in order to properly capture which local regions the generator should change while keeping the other regions unchanged. In this manner, our approach allows the generator and the discriminator to collaborate with each other to improve the image translation quality. In our experiments, we validate the effectiveness and versatility of our proposed CAM-consistency loss by applying it to several representative models for facial image editing, such as StarGAN, AttGAN, and STGAN.

关键词： Training computer vision Computational modeling computer architecture Predictive models Generative adversarial networks Generators

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Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

Non-Salient Region Object Mining for Weakly Supervised Seman...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yao, Yazhou Chen, Tao Xie, Guo-Sen Zhang, Chuanyi Shen, Fumin Wu, Qi Tang, Zhenmin Zhang, Jian Nanjing Univ Sci & Technol Nanjing Peoples R China Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Univ Elect Sci & Technol China Chengdu Peoples R China Univ Adelaide Adelaide SA Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image's salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network's ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods.

关键词： Image segmentation computer vision Codes Annotations Image edge detection Semantics Cognition

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SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation

SCF-Net: Learning Spatial Contextual Features for Large-Scal...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fan, Siqi Dong, Qiulei Zhu, Fenghua Lv, Yisheng Ye, Peijun Wang, Fei-Yue CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CASIA Natl Lab Pattern Recognit Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

How to learn effective features from large-scale point clouds for semantic segmentation has attracted increasing attention in recent years. Addressing this problem, we propose a learnable module that learns Spatial Contextual Features from large-scale point clouds, called SCF in this paper. The proposed module mainly consists of three blocks, including the local polar representation block, the dual-distance attentive pooling block, and the global contextual feature block. For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud. The proposed module could be easily embedded into various network architectures for point cloud segmentation, naturally resulting in a new 3D semantic segmentation network with an encoder-decoder architecture, called SCF-Net in this work. Extensive experimental results on two public datasets demonstrate that the proposed SCF-Net performs better than several state-of-the-art methods in most cases.

关键词： computer vision Three-dimensional displays Semantics computer architecture Network architecture pattern recognition

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Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval

Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Ima...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fuentes, Anibal Saavedra, Jose M. Impresee Inc 600 Calif St San Francisco CA 94108 USA

ISBN: (纸本)9781665448994

Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of-the-art results.

关键词： Training Visualization computer vision Image color analysis conferences Image retrieval computer architecture

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ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

ORDisCo: Effective and Efficient Usage of Incremental Unlabe...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Liyuan Yang, Kuo Li, Chongxuan Hong, Lanqing Li, Zhenguo Zhu, Jun Tsinghua Univ BNRist Ctr Inst AI Dept Comp Sci & TechTHBI Lab Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines.

关键词： Deep learning computer vision Systematics Semisupervised learning Benchmark testing Generative adversarial networks Generators

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