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Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data

Commonality in Natural Images Rescues GANs: Pretraining GANs...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Baek, Kyungjune Shim, Hyunjung Yonsei Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transfer learning for GANs successfully improves generation performance under low-shot regimes. However, existing studies show that the pretrained model using a single benchmark dataset is not generalized to various target datasets. More importantly, the pretrained model can be vulnerable to copyright or privacy risks as membership inference attack advances. To resolve both issues, we propose an effective and unbiased data synthesizer, namely Primitives-PS, inspired by the generic characteristics of natural images. Specifically, we utilize 1) the generic statistics on the frequency magnitude spectrum, 2) the elementary shape (i.e., image composition via elementary shapes) for representing the structure information, and 3) the existence of saliency as prior. Since our synthesizer only considers the generic properties of natural images, the single model pretrained on our dataset can be consistently transferred to various target datasets, and even outperforms the previous methods pretrained with the natural images in terms of Frechet inception distance. Extensive analysis, ablation study, and evaluations demonstrate that each component of our data synthesizer is effective, and provide insights on the desirable nature of the pretrained model for the transferability of GANs.

关键词： Privacy computer vision Image resolution Shape Synthesizers Computational modeling Transfer learning

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An Empirical Study of Vehicle Re-Identification on the AI City Challenge

An Empirical Study of Vehicle Re-Identification on the AI Ci...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Luo, Hao Chen, Weihua Xu, Xianzhe Gu, Jianyang Zhang, Yuqi Liu, Chong Jiang, Yiqi He, Shuting Wang, Fan Li, Hao Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://***/michuanhaohao/AICITY2021_Track2_DMT.

关键词： Training computer vision conferences Computational modeling Urban areas Training data Data models

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Is Multimodal vision Supervision Beneficial to Language?

Is Multimodal Vision Supervision Beneficial to Language?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Avinash Madasu Vasudev Lal Department of Computer Science UNC Chapel Hill USA Cognitive Computing Research Intel Labs USA

vision (image & video) - Language (VL) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks like image-retrieval, video-retrieval, visual question answering etc. These models are trained in an unsupervised way and greatly benefit from the complementary modality supervision. In this paper, we explore if the language representations trained using vision supervision perform better than vanilla language representations on Natural Language Understanding and commonsense reasoning benchmarks. We experiment with a diverse set of image-text models such as ALBEF, BLIP, METER and video-text models like ALPRO, Frozen in Time, VIOLET. We compare the performance of language representations of stand-alone text encoders of these models to the language representations of text encoders learnt through vision supervision. Our experiments suggest that vanilla language representations show superior performance on most of the tasks. These results shed light on the current drawbacks of the vision-language models. The code is available at https://***/avinashsai/MML

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MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices

MobileHumanPose: Toward real-time 3D human pose estimation i...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Choi, Sangbum Choi, Seokeon Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

Currently, 3D pose estimation methods are not compatible with a variety of low computational power devices because of efficiency and accuracy. In this paper, we revisit a pose estimation architecture from a viewpoint of both efficiency and accuracy. We propose a mobile-friendly model, MobileHumanPose, for real-time 3D human pose estimation from a single RGB image. This model consists of the modified MobileNetV2 backbone, a parametric activation function, and the skip concatenation inspired by U-Net. Especially, the skip concatenation structure improves accuracy by propagating richer features with negligible computational power. Our model achieves not only comparable performance to the state-of-the-art models but also has a seven times smaller model size compared to the ResNet-50 based model. In addition, our extra small model reduces inference time by 12.2ms on Galaxy S20 CPU, which is suitable for real-time 3D human pose estimation in mobile applications. The source code is available at: https://***/SangbumChoi/MobileHumanPose.

关键词： Performance evaluation Solid modeling Three-dimensional displays Computational modeling Pose estimation computer architecture Real-time systems

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VideoSAGE: Video Summarization with Graph Representation Learning

VideoSAGE: Video Summarization with Graph Representation Lea...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jose M. Rojas Chaves Subarna Tripathi Intel Corporation Intel Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that are within a specified temporal distance. We then formulate the video summarization task as a binary node classification problem, precisely classifying video frames whether they should belong to the output summary video. A graph constructed this way aims to capture long-range interactions among video frames, and the sparsity ensures the model trains without hitting the memory and compute bottleneck. Experiments on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed nimble model compared to existing state-of-the-art summarization approaches while being one order of magnitude more efficient in compute time and memory.

关键词： Representation learning computer vision Computational modeling conferences Memory management pattern recognition

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End-to-End Multi-Person Pose Estimation with Transformers

End-to-End Multi-Person Pose Estimation with Transformers

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shi, Dahu Wei, Xing Li, Liangqi Ren, Ye Tan, Wenming Hikvis Res Inst Hangzhou Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current methods of multi-person pose estimation typically treat the localization and association of body joints separately. In this paper, we propose the first filly end-to-end multi-person Pose Estimation framework with TRansformers, termed PETR. Our method views pose estimation as a hierarchical set prediction problem and effectively removes the need for many hand-crafted modules like Rol cropping, NMS and grouping post-processing. In PETR, multiple pose queries are learned to directly reason a set offull-body poses. Then a joint decoder is utilized to further refine the poses by exploring the kinematic relations between body joints. With the attention mechanism, the proposed method is able to adaptively attend to the features most relevant to target keypoints, which largely overcomes the feature misalignment difficulty in pose estimation and improves the performance considerably. Extensive experiments on the MS COCO and CrowdPose benchmarks show that PETR plays favorably against state-of-the-art approaches in terms of both accuracy and efficiency. The code and models are available at https://***/hikvision-research/opera.

关键词： Location awareness computer vision Image analysis Codes Pose estimation Kinematics Benchmark testing

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jiahe Steven Lee Wynne Hsu Mong Li Lee Institute of Data Science National University of Singapore Centre for Trusted Internet and Community National University of Singapore

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： computer vision conferences Benchmark testing Transformers Feature extraction pattern recognition Fake news

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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

NTIRE 2021 Multi-modal Aerial View Object Classification Cha...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Jerrick Inkawhich, Nathan Nina, Oliver Timofte, Radu Duan, Yuru Li, Gongzhe Geng, Xueli Cai, Huanqia Air Force Res Lab Albuquerque NM 87117 USA Univ Illinois Urbana IL 61801 USA Duke Univ Durham NC 27706 USA Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Changan Campus Xian Shaanxi Peoples R China Beihang Univ Beijing Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Xian Peoples R China Dengzhuang South Rd Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO and SAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition.

关键词： Image sensors conferences Radar tracking Radar polarimetry Sensors pattern recognition Task analysis

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A ConvNet for the 2020s

A ConvNet for the 2020s

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Zhuang Mao, Hanzi Wu, Chao-Yuan Feichtenhofer, Christoph Darrell, Trevor Xie, Saining Facebook AI Res FAIR Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The "Roaring 20s" of visual recognition began with the introduction of vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.

关键词： computer vision Image segmentation Visualization Computational modeling Scalability Semantics Transformers

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Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning

Self-Sustaining Representation Expansion for Non-Exemplar Cl...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Kai Zhai, Wei Cao, Yang Luo, Jiebo Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Univ Rochester Rochester NY USA Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimization and feature retention can only be achieved under supervision from new classes. To address this problem, we propose a novel self-sustaining representation expansion scheme. Our scheme consists of a structure reorganization strategy that fuses main-branch expansion and side-branch updating to maintain the old features, and a main-branch distillation scheme to transfer the invariant knowledge. Furthermore, a prototype selection mechanism is proposed to enhance the discrimination between the old and new classes by selectively incorporating new samples into the distillation process. Extensive experiments on three benchmarks demonstrate significant incremental performance, outperforming the state-of-the-art methods by a margin of 3%, 3% and 6%, respectively.

关键词： computer vision Fuses Prototypes Benchmark testing pattern recognition Task analysis Optimization

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