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共 21180 条记录，以下是541-550 订阅

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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...

引用

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

来源：评论

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DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos

DNeRV: Modeling Inherent Dynamics via Difference Neural Repr...

引用

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

作者： Zhao, Qi Asif, M. Salman Ma, Zhan Nanjing Univ Nanjing Peoples R China Univ Calif Riverside CA USA

ISBN: (纸本)9798350301298

Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for 960 x 1920 videos.

关键词： Low-level vision

来源：评论

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Outsmarting Biometric Imposters: Enhancing Iris-recognition System Security through Physical Adversarial Example Generation and PAD Fine-Tuning

Outsmarting Biometric Imposters: Enhancing Iris-Recognition ...

引用

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

作者： Ogino, Yuka Kakizaki, Kazuya Toizumi, Takahiro Ito, Atsushi NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350365474

In this paper, we address the vulnerabilities of iris recognition systems to both image-based impersonation attacks and Presentation Attacks (PAs) in physical environments. While existing Presentation Attack Detection (PAD) methods have been effective against PAs, they remain susceptible to adversarial examples. We propose a combination of physical adversarial attacks tailored to iris recognition and PAD, and also propose a defense method against them. Our attack methods involve a physical impersonation attack using adversarial perturbation on the iris region and a physical PAD evading attack using an adversarial patch on the pupil region. We demonstrate the high transferability and effectiveness of our attacks on multiple PA instruments in digital and distinct physical environments using multiple recognition engines. To counteract these attacks, we develop a defense method for PAD involving adversarial fine-tuning against both the physical attacks. This defense method successfully reduces the PAD evasion attack success rate from 71.5% to 21.0% in physical environments and ultimately lowers the overall physical impersonation success rate from 58.0% to 19.5%. Our proposed method lays the groundwork for developing more robust and secure iris recognition systems with increased protection against sophisticated PAs.

关键词： Optical character recognition

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Interactive and Explainable Region-guided Radiology Report Generation

Interactive and Explainable Region-guided Radiology Report G...

引用

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

作者： Tanida, Tim Muller, Philip Kaissis, Georgios Rueckert, Daniel Tech Univ Munich Munich Germany Helmholtz Zentrum Munich Munich Germany Imperial Coll London London England

ISBN: (纸本)9798350301298

The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://***/ttanida/rgrg.

关键词： cell microscopy Medical and biological vision

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Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

Generate Like Experts: Multi-Stage Font Generation by Incorp...

引用

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

作者： Fu, Bin Yu, Fanghua Liu, Anran Wang, Zixuan Wen, Jie He, Junjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples, which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods, which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers, in this paper, we model font generation as a multi-stage generative process. Specifically, as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces, we are able to incorporate the font transfer process into these models. Based on this observation, we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image, and the font transfer stage subsequently transforms the source font to the target font. Finally, the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process, we construct our font generation framework, named MSD-Font, with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://***/fubinfb/MSD-Font

关键词： Diffusion Model Few-shot Font Generation Probabilistic Generative Model

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Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations

Improving Visual Grounding by Encouraging Consistent Gradien...

引用

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

作者： Yang, Ziyan Kafle, Kushal Dernoncourt, Franck Ordonez, Vicente Rice Univ Houston TX 77005 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350301298

We propose a margin-based loss for tuning joint vision-language models so that their gradient-based explanations are consistent with region-level annotations provided by humans for relatively smaller grounding datasets. We refer to this objective as Attention Mask Consistency (AMC) and demonstrate that it produces superior visual grounding results than previous methods that rely on using vision-language models to score the outputs of object detectors. Particularly, a model trained with AMC on top of standard vision-language modeling objectives obtains a state-of-the-art accuracy of 86.49% in the Flickr30k visual grounding benchmark, an absolute improvement of 5.38% when compared to the best previous model trained under the same level of supervision. Our approach also performs exceedingly well on established benchmarks for referring expression comprehension where it obtains 80.34% accuracy in the easy test of RefCOCO+, and 64.55% in the difficult split. AMC is effective, easy to implement, and is general as it can be adopted by any vision-language model, and can use any type of region annotations.

关键词： language reasoning vision

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PMatch: Paired Masked Image Modeling for Dense Geometric Matching

PMatch: Paired Masked Image Modeling for Dense Geometric Mat...

引用

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

作者： Zhu, Shengjie Liu, Xiaoming Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9798350301298

Dense geometric matching determines the dense pixel-wise correspondence between a source and support image corresponding to the same 3D structure. Prior works employ an encoder of transformer blocks to correlate the two-frame features. However, existing monocular pretraining tasks, e.g., image classification, and masked image modeling (MIM), can not pretrain the cross-frame module, yielding less optimal performance. To resolve this, we reformulate the MIM from reconstructing a single masked image to reconstructing a pair of masked images, enabling the pretraining of transformer module. Additionally, we incorporate a decoder into pretraining for improved upsampling results. Further, to be robust to the textureless area, we propose a novel cross-frame global matching module (CFGM). Since the most textureless area is planar surfaces, we propose a homography loss to further regularize its learning. Combined together, we achieve the State-of-The-Art (SoTA) performance on geometric matching. Codes and models are available at https://***/ShngJZ/PMatch.

关键词： Low-level vision

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Image as a Foreign Language: BEIT Pretraining for vision and vision-Language Tasks

Image as a Foreign Language: BEIT Pretraining for Vision and...

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

作者： Wang, Wenhui Bao, Hangbo Dong, Li Bjorck, Johan Peng, Zhiliang Liu, Qiang Aggarwal, Kriti Mohammed, Owais Khan Singhal, Saksham Som, Subhojit Wei, Furu Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9798350301298

A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEIT-3, which achieves excellent transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We use Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner. Experimental results show that BEIT-3 obtains remarkable performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO).

关键词： language reasoning 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...

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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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GRAFIQS: Face Image Quality Assessment Using Gradient Magnitudes

GRAFIQS: Face Image Quality Assessment Using Gradient Magnit...

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

作者： Kolf, Jan Niklas Damer, Naser Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350365474

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.

关键词： Biometrics computer vision Face Image Quality Assessment Face recognition

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