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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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Few-shot Image Generation via Cross-domain Correspondence

Few-shot Image Generation via Cross-domain Correspondence

引用

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

作者： Ojha, Utkarsh Li, Yijun Lu, Jingwan Efros, Alexei A. Lee, Yong Jae Shechtman, Eli Zhang, Richard Adobe Res San Jose CA 95110 USA Univ Calif Davis Davis CA 95616 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and differences between instances in the source via a novel cross-domain distance consistency loss. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space. With extensive results in both photorealistic and non-photorealistic domains, we demonstrate qualitatively and quantitatively that our few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods.

关键词： Training computer vision Image synthesis Computational modeling pattern recognition

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SemiCVT: Semi-Supervised Convolutional vision Transformer for Semantic Segmentation

SemiCVT: Semi-Supervised Convolutional Vision Transformer fo...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Huimin Huang Shiao Xie Lanfen Lin Ruofeng Tong Yen-Wei Chen Yuexiang Li Hong Wang Yawen Huang Yefeng Zheng Zhejiang University Zhejiang Lab Ritsumeikan University Tencent Jarvis Lab

Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples. These successes concentrate on the pixel-wise consistency by using convolutional neural networks (CNNs) but fail to address both global learning capability and class-level features for unlabeled data. Recent works raise a new trend that Transformer achieves superior performance on the entire feature map in various tasks. In this paper, we unify the current dominant Mean-Teacher approaches by reconciling intra-model and inter-model properties for semi-supervised segmentation to produce a novel algorithm, SemiCVT, that absorbs the quintessence of CNNs and Transformer in a comprehensive way. Specifically, we first design a parallel CNN-Transformer architecture (CVT) with introducing an intra-model local-global interaction schema (LGI) in Fourier domain for full integration. The inter-model class-wise consistency is further presented to complement the class-level statistics of CNNs and Transformer in a cross-teaching manner. Extensive empirical evidence shows that SemiCVT yields consistent improvements over the state-of-the-art methods in two public benchmarks.

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Learning Discriminative Representations for Skeleton Based Action recognition

Learning Discriminative Representations for Skeleton Based A...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Huanyu Zhou Qingjie Liu Yunhong Wang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Zhongguancun Laboratory Hangzhou Innovation Institute of Beihang University

Human action recognition aims at classifying the category of human action from a segment of a video. Recently, people have dived into designing GCN-based models to extract features from skeletons for performing this task, because skeleton representations are much more efficient and robust than other modalities such as RGB frames. However, when employing the skeleton data, some important clues like related items are also discarded. It results in some ambiguous actions that are hard to be distinguished and tend to be misclassified. To alleviate this problem, we propose an auxiliary feature refinement head (FR Head), which consists of spatial-temporal decoupling and contrastive feature refinement, to obtain discriminative representations of skeletons. Ambiguous samples are dynamically discovered and calibrated in the feature space. Furthermore, FR Head could be imposed on different stages of GCNs to build a multi-level refinement for stronger supervision. Extensive experiments are conducted on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets. Our proposed models obtain competitive results from state-of-the-art methods and can help to discriminate those ambiguous samples. Codes are available at https://***/zhysora/FR-Head.

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X-Pruner: eXplainable Pruning for vision Transformers

X-Pruner: eXplainable Pruning for Vision Transformers

引用

conference on computer vision and pattern recognition (cvpr)

作者： Lu Yu Wei Xiang James Cook University La Trobe University

Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requirements, making them impractical for deployment on edge platforms. Recent studies have proposed to prune transformers in an unexplainable manner, which overlook the relationship between internal units of the model and the target class, thereby leading to inferior performance. To alleviate this problem, we propose a novel explainable pruning framework dubbed X-Pruner, which is designed by considering the explainability of the pruning criterion. Specifically, to measure each prunable unit's contribution to predicting each target class, a novel explainability-aware mask is proposed and learned in an end-to-end manner. Then, to preserve the most informative units and learn the layer-wise pruning rate, we adaptively search the layer-wise threshold that differentiates between unpruned and pruned units based on their explainability-aware mask values. To verify and evaluate our method, we apply the X-Pruner on representative transformer models including the DeiT and Swin Transformer. Comprehensive simulation results demonstrate that the proposed X-Pruner outperforms the state-of-the-art black-box methods with significantly reduced computational costs and slight performance degradation. Code is available at https://***/vickyyu90/XPruner.

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Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food

Nutrition5k: Towards Automatic Nutritional Understanding of ...

引用

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

作者： Thames, Quin Karpur, Arjun Norris, Wade Xia, Fangting Panait, Liviu Weyand, Tobias Sim, Jack Google Res Mountain View CA 94043 USA Percept Labs Great Falls VA USA

ISBN: (纸本)9781665445092

Understanding the nutritional content of food from visual data is a challenging computer vision problem, with the potential to have a positive and widespread impact on public health. Studies in this area are limited to existing datasets in the field that lack sufficient diversity or labels required for training models with nutritional understanding capability. We introduce Nutrition5k, a novel dataset of 5k diverse, real world food dishes with corresponding video streams, depth images, component weights, and high accuracy nutritional content annotation. We demonstrate the potential of this dataset by training a computer vision algorithm capable of predicting the caloric and macronutrient values of a complex, real world dish at an accuracy that outperforms professional nutritionists. Further we present a baseline for incorporating depth sensor data to improve nutrition predictions. We release Nutrition5k in the hope that it will accelerate innovation in the space of nutritional understanding.

关键词： Training computer vision Visualization Technological innovation Annotations Estimation Streaming media

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Dual-bridging with Adversarial Noise Generation for Domain Adaptive rPPG Estimation

Dual-bridging with Adversarial Noise Generation for Domain A...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jingda Du Si-Qi Liu Bochao Zhang Pong C. Yuen Department of Computer Science Hong Kong Baptist University Hong Kong

The remote photoplethysmography (rPPG) technique can estimate pulse-related metrics (e.g. heart rate and respiratory rate) from facial videos and has a high potential for health monitoring. The latest deep rPPG methods can model in-distribution noise due to head motion, video compression, etc., and estimate high-quality rPPG signals under similar scenarios. However, deep rPPG models may not generalize well to the target test domain with unseen noise and distortions. In this paper, to improve the generalization ability of rPPG models, we propose a dual-bridging network to reduce the domain discrepancy by aligning intermediate domains and synthesizing the target noise in the source domain for better noise reduction. To comprehensively explore the target domain noise, we propose a novel adversarial noise generation in which the noise generator indirectly competes with the noise reducer. To further improve the robustness of the noise reducer, we propose hard noise pattern mining to encourage the generator to learn hard noise patterns contained in the target domain features. We evaluated the proposed method on three public datasets with different types of interferences. Under different crossdomain scenarios, the comprehensive results show the effectiveness of our method.

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AdaBins: Depth Estimation Using Adaptive Bins

AdaBins: Depth Estimation Using Adaptive Bins

引用

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

作者： Bhat, Shariq Farooq Alhashim, Ibraheem Wonka, Peter KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665445092

We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.

关键词： Measurement Image segmentation Three-dimensional displays Image resolution Estimation computer architecture Transformers

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Adaptive Convolutions for Structure-Aware Style Transfer

Adaptive Convolutions for Structure-Aware Style Transfer

引用

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

作者： Chandran, Prashanth Zoss, Gaspard Gotardo, Paulo Gross, Markus Bradley, Derek DisneyRes Studios Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781665445092

Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content. The state of the art in neural style transfer is based on Adaptive Instance Normalization (AdaIN), a technique that transfers the statistical properties of style features to a content image, and can transfer a large number of styles in real time. However AdaIN is a global operation;thus local geometric structures in the style image are often ignored during the transfer. We propose Adaptive Convolutions (AdaConv), a generic extension of AdaIN, to allow for the simultaneous transfer of both statistical and structural styles in real time. Apart from style transfer, our method can also be readily extended to style-based image generation, and other tasks where AdaIN has already been adopted.

关键词： computer vision Image synthesis Real-time systems Weaving pattern recognition Decoding Task analysis

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CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback

CoSMo: Content-Style Modulation for Image Retrieval with Tex...

引用

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

作者： Lee, Seungmin Kim, Dongwan Han, Bohyung Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665445092

We tackle the task of image retrieval with text feedback, where a reference image and modifier text are combined to identify the desired target image. We focus on designing an image-text compositor, i.e., integrating multi-modal inputs to produce a representation similar to that of the target image. In our algorithm, Content-Style Modulation (CoSMo), we approach this challenge by introducing two modules based on deep neural networks: the content and style modulators. The content modulator performs local updates to the reference image feature after normalizing the style of the image, where a disentangled multi-modal non-local block is employed to achieve the desired content modifications. Then, the style modulator reintroduces global style information to the updated feature. We provide an in-depth view of our algorithm and its design choices, and show that it accomplishes outstanding performance on multiple image-text retrieval benchmarks.

关键词： Deep learning computer vision Codes Image retrieval Modulation Benchmark testing pattern recognition

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Transferable Adversarial Attacks on vision Transformers with Token Gradient Regularization

Transferable Adversarial Attacks on Vision Transformers with...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jianping Zhang Yizhan Huang Weibin Wu Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong School of Software Engineering Sun Yat-sen University

vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based at-tacks, our TGR offers a performance improvement of 8.8% on average.

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