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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是511-520 订阅

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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...

引用

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

作者： Lee, Jiahe Steven Hsu, Wynne Lee, Mong Li Natl Univ Singapore Inst Data Sci Singapore Singapore Natl Univ Singapore Ctr Trusted Internet & Community Singapore Singapore

ISBN: (纸本)9798350365474

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： image copy detection image retrieval

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Federated Learning with a Single Shared Image

Federated Learning with a Single Shared Image

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

作者： Soni, Sunny Saeed, Aaqib Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9798350365474

Federated Learning (FL) enables multiple machines to collaboratively train a machine learning model without sharing of private training data. Yet, especially for heterogeneous models, a key bottleneck remains the transfer of knowledge gained from each client model with the server. One popular method, FedDF, uses distillation to tackle this task with the use of a common, shared dataset on which predictions are exchanged. However, in many contexts such a dataset might be difficult to acquire due to privacy and the clients might not allow for storage of a large shared dataset. To this end, in this paper, we introduce a new method that improves this knowledge distillation method to only rely on a single shared image between clients and server. In particular, we propose a novel adaptive dataset pruning algorithm that selects the most informative crops generated from only a single image. With this, we show that federated learning with distillation under a limited shared dataset budget works better by using a single image compared to multiple individual ones. Finally, we extend our approach to allow for training heterogeneous client architectures by incorporating a non-uniform distillation schedule and client-model mirroring on the server side.

关键词： computer vision Federated Learning Limited Data Representation Learning

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Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for vision Applications

Efficient Deformable ConvNets: Rethinking Dynamic and Sparse...

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

作者： Xiong, Yuwen Li, Zhiqi Chen, Yuntao Wang, Feng Zhu, Xizhou Luo, Jiapeng Wang, Wenhai Lu, Tong Li, Hongsheng Qiao, Yu Lu, Lewei Zhou, Jie Dai, Jifeng Univ Toronto Toronto ON Canada Shanghai AI Lab OpenGVLab Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Chinese Acad Sci CAIR HKISI Beijing Peoples R China Tsinghua Univ Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353013;9798350353006

We introduce Deformable Convolution v4 (DCNv4), a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor, DCNv3, with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2. optimizing memory access to minimize redundant operations for speedup. These improvements result in a significantly faster convergence compared to DCNv3 and a substantial increase in processing speed, with DCNv4 achieving more than three times the forward speed. DCNv4 demonstrates exceptional performance across various tasks, including image classification, instance and semantic segmentation, and notably, image generation. When integrated into generative models like U-Net in the latent diffusion model, DCNv4 outperforms its baseline, underscoring its possibility to enhance generative models. In practical applications, replacing DCNv3 with DCNv4 in the InternImage model to create FlashInternImage results in up to 80% speed increase and further performance improvement without further modifications. The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

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

作者： Hirohashi, Yuki Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu OMRON Corp Kyoto Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp's 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp's effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Prompt Learning vision-and-Language Model

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Doubly Right Object recognition: A Why Prompt for Visual Rationales

Doubly Right Object Recognition: A Why Prompt for Visual Rat...

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

作者： Mao, Chengzhi Teotial, Revant Sundari, Amrutha Menon, Sachit Yang, Junfeng Wang, Xin Vondrick, Carl Columbia Univ New York NY 10027 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a "doubly right" object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a "why prompt," which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets.

关键词： Explainable computer vision

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Unveiling the Anomalies in an Ever-ChangingWorld: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning

Unveiling the Anomalies in an Ever-ChangingWorld: A Benchmar...

引用

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

作者： Bugarin, Nikola Bugaric, Jovana Barusco, Manuel Pezze, Davide Dalle Susto, Gian Antonio Univ Padua Padua Italy

ISBN: (纸本)9798350365474

Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Continual Learning setting, where new data arrives over time and the goal is to perform well on new and old data. We implement several state-of-the-art techniques to solve the Anomaly Detection problem in the classic setting and adapt them to work in the Continual Learning setting. To validate the approaches, we use a real-world dataset of images with pixel-based anomalies to provide a reliable benchmark and serve as a foundation for further advancements in the field. We provide a comprehensive analysis, discussing which Anomaly Detection methods and which families of approaches seem more suitable for the Continual Learning setting.

关键词： Anomaly Detection computer vision Continual Learning

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Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action recognition

Neural Koopman Pooling: Control-Inspired Temporal Dynamics E...

引用

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

作者： Wang, Xinghan Xu, Xin Mu, Yadong Peking Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Skeleton-based human action recognition is becoming increasingly important in a variety of fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal features, and use temporal average/max pooling to aggregate the information. However, these pooling methods fail to capture high-order dynamics information. To address the problem, we propose a plug-and-play module called Koopman pooling, which is a parameterized high-order pooling technique based on Koopman theory. The Koopman operator linearizes a non-linear dynamics system, thus providing a way to represent the complex system through the dynamics matrix, which can be used for classification. We also propose an eigenvalue normalization method to encourage the learned dynamics to be non-decaying and stable. Besides, we also show that our Koopman pooling framework can be easily extended to one-shot action recognition when combined with Dynamic Mode Decomposition. The proposed method is evaluated on three benchmark datasets, namely NTU RGB+D 60, 120 and NW-UCLA. Our experiments clearly demonstrate that Koopman pooling significantly improves the performance under both full-dataset and one-shot settings.

关键词： detection recognition: Categorization retrieval

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Video Representation Learning for Conversational Facial Expression recognition Guided by Multiple View Reconstruction

Video Representation Learning for Conversational Facial Expr...

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

作者： Strizhkova, Valeriya Ferrari, Laura M. Kachmar, Hadi Dantcheva, Antitza Bremond, Francois INRIA Paris France Univ Cote dAzur Nice France

ISBN: (纸本)9798350365474

Conversational facial expression recognition entails challenges such as handling of facial dynamics, small available datasets, low-intensity and fine-grained emotional expressions and extreme face angle. Towards addressing these challenges, we propose the Masking Action Units and Reconstructing multiple Angles (MAURA) pre-training. MAURA is an efficient self-supervised method that permits the use of small datasets, while preserving end-toend conversational facial expression recognition with vision Transformer. MAURA masks videos using the location with active Action Units and reconstructs synchronized multi-view videos, thus learning the dependencies between muscle movements and encoding information, which might only be visible in few frames and/or in certain views. Based on one view (e.g., frontal), the encoder reconstructs other views (e.g., top, down, laterals). Such masking and reconstructing strategy provides a powerful representation, beneficial in facial expression downstream tasks. Our experimental analysis shows that we consistently outperform the state-of-the-art in the challenging settings of low-intensity and fine-grained conversational facial expression recognition on four datasets including in-the-wild DFEW, CMU-MOSEI, MFA and multi-view MEAD. Our results suggest that MAURA is able to learn robust and generic video representations.

关键词： Signal encoding

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Making use of unlabeled data: Comparing strategies for marine animal detection in long-tailed datasets using self-supervised and semi-supervised pre-training

Making use of unlabeled data: Comparing strategies for marin...

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

作者： Sharma, Tarun Cline, Danelle E. Edgington, Duane CALTECH Pasadena CA 91125 USA MBARI Moss Landing CA USA

ISBN: (纸本)9798350365474

This paper discusses strategies for object detection in marine images from a practitioner's perspective working with real-world long-tail distributed datasets with a large amount of additional unlabeled data on hand. The paper discusses the benefits of separating the localization and classification stages, making the case for robustness in localization through the amalgamation of additional datasets inspired by a widely used approach by practitioners in the camera-trap literature. For the classification stage, the paper compares strategies to use additional unlabeled data, comparing supervised, supervised iteratively, self-supervised, and semi-supervised pre-training approaches. Our findings reveal that semi-supervised pre-training, followed by supervised fine-tuning, yields a significantly improved balanced performance across the long-tail distribution, albeit occasionally with a trade-off in overall accuracy. These insights are validated through experiments on two real-world long-tailed underwater datasets collected by the Monterey Bay Aquarium Research Institute (MBARI).

关键词： computer vision conservation technology long-tail long-tailed distribution marine imaging real-world datasets Self-supervised learning semi-supervised learning underwater animal detection unlabeled data

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Improving Image recognition by Retrieving from Web-Scale Image-Text Data

Improving Image Recognition by Retrieving from Web-Scale Ima...

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

作者： Iscen, Ahmet Fathi, Alireza Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We also thoroughly study various ways of constructing the memory dataset. Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, and show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.

关键词： detection recognition: Categorization retrieval

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