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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 29687 条记录，以下是4671-4680 订阅

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Generating Non-Stationary Textures Using Self-Rectification

Generating Non-Stationary Textures Using Self-Rectification

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yang Zhou Rongjun Xiao Dani Lischinski Daniel Cohen-Or Hui Huang Shenzhen University The Hebrew University of Jerusalem Tel Aviv University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel two-step approach wherein users first modify a reference texture using standard image editing tools, yielding an initial rough target for the synthesis. Subsequently, our proposed method, termed “self-rectification”, automatically refines this target into a coherent, seamless texture, while faithfully preserving the distinct visual characteristics of the reference exemplar. Our method leverages a pretrained diffusion network, and uses self-attention mechanisms, to grad-ually align the synthesized texture with the reference, en-suring the retention of the structures in the provided target. Through experimental validation, our approach ex-hibits exceptional proficiency in handling non-stationary textures, demonstrating significant advancements in texture synthesis when compared to existing state-of-the-art techniques. Code is available at https://***/xiaorongjun000/Self-Rectification

关键词： Visualization computer vision Codes Semantics Controllability pattern recognition Standards

来源：评论

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CORES: Convolutional Response-based Score for Out-of-distribution Detection

CORES: Convolutional Response-based Score for Out-of-distrib...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Keke Tang Chao Hou Weilong Peng Runnan Chen Peican Zhu Wenping Wang Zhihong Tian Guangzhou University University of Hong Kong Northwestern Polytechnical University Texas A&M University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples, posing significant challenges in real-world applications. Capitalizing on the observation that responses on convolutional kernels are generally more pronounced for in-distribution (ID) samples than for OOD ones, this paper proposes the COnvolutional REsponse-based Score (CORES) to exploit these discrepancies for OOD detection. Initially, CORES delves into the extremities of convolutional responses by considering both their magnitude and the frequency of significant values. Moreover, through backtracking from the most prominent predictions, CORES effectively pinpoints sample-relevant kernels across different layers. These kernels, which exhibit a strong correlation to input samples, are integral to CORES's OOD detection capability. Comprehensive experiments across various ID and OOD settings demonstrate CORES's effectiveness in OOD detection and its superiority to the state-of-the-art methods.

关键词： computer vision Backtracking Correlation Fitting Artificial neural networks pattern recognition Security

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Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization

Selectively Informative Description can Reduce Undesired Emb...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jimyeong Kim Jungwon Park Wonjong Rhee Department of Intelligence and Information Seoul National University IPAI Seoul National University RICS Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In text-to-image personalization, a timely and crucial challenge is the tendency of generated images overfitting to the biases present in the reference images. We initiate our study with a comprehensive categorization of the biases into background, nearby-object, tied-object, substance (in style re-contextualization), and pose biases. These biases manifest in the generated images due to their entanglement into the subject embedding. This undesired embedding entanglement not only results in the reflection of biases from the reference images into the generated images but also notably diminishes the alignment of the generated images with the given generation prompt. To address this challenge, we propose SID (Selectively Informative Description), a text description strategy that deviates from the prevalent approach of only characterizing the subject's class identification. SID is generated utilizing multimodal GPT-4 and can be seamlessly integrated into optimization-based models. We present comprehensive experimental results along with analyses of cross-attention maps, subject-alignment, non-subject-disentanglement, and text-alignment.

关键词： computer vision Text to image Reflection pattern recognition

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Adversarial Threats to DeepFake Detection: A Practical Perspective

Adversarial Threats to DeepFake Detection: A Practical Persp...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Neekhara, Paarth Dolhansky, Brian Bitton, Joanna Ferrer, Cristian Canton Univ Calif San Diego La Jolla CA 92093 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665448994

Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the credibility of social media platforms and other media sharing web sites. State-of-the art DeepFake detection techniques rely on neural network based classification models which are known to be vulnerable to adversarial examples. In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point. We perform adversarial attacks on DeepFake detectors in a black box setting where the adversary does not have complete knowledge of the classification models. We study the extent to which adversarial perturbations transfer across different models and propose techniques to improve the transferability of adversarial examples. We also create more accessible attacks using Universal Adversarial Perturbations which pose a very feasible attack scenario since they can be easily shared amongst attackers. We perform our evaluations on the winning entries of the DeepFake Detection Challenge (DFDC) and demonstrate that they can be easily bypassed in a practical attack scenario by designing transferable and accessible adversarial attacks.(1)

关键词： Social networking (online) Perturbation methods Neural networks Multimedia Web sites Detectors pattern recognition Security

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Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance

Improving Subject-Driven Image Synthesis with Subject-Agnost...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Kelvin C.K. Chan Yang Zhao Xuhui Jia Ming-Hsuan Yang Huisheng Wang Google

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In subject-driven text-to-image synthesis, the synthesis process tends to be heavily influenced by the reference images provided by users, often overlooking crucial attributes detailed in the text prompt. In this work, we propose Subject-Agnostic Guidance (SAG), a simple yet effective solution to remedy the problem. We show that through constructing a subject-agnostic condition and applying our proposed dual classifier-free guidance, one could obtain outputs consistent with both the given subject and input text prompts. We validate the efficacy of our approach through both optimization-based and encoder-based methods. Additionally, we demonstrate its applicability in second-order customization methods, where an encoder-based model is fine-tuned with DreamBooth. Our approach is conceptually simple and requires only minimal code modifications, but leads to substantial quality improvements, as evidenced by our evaluations and user studies.

关键词： Training computer vision Codes Image synthesis Text to image pattern recognition

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Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning

Regularized Parameter Uncertainty for Improving Generalizati...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Pehuen Moure Longbiao Cheng Joachim Ott Zuowen Wang Shih-Chii Liu Institute of Neuroinformatics ETH Zurich and University of Zurich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In order for reinforcement learning (RL) agents to be deployed in real-world environments, they must be able to generalize to unseen environments. However, RL struggles with out-of-distribution generalization, often due to overfitting the particulars of the training environment. Although regularization techniques from supervised learning can be applied to avoid over-fitting, the differences between supervised learning and RL limit their application. To address this, we propose the Signal-to-Noise Ratio regulated Parameter Uncertainty Network (SNR PUN) for RL. We introduce SNR as a new measure of regularizing the parameter uncertainty of a network and provide a formal analysis explaining why SNR regularization works well for RL. We demonstrate the effectiveness of our proposed method to generalize in several simulated environments; and in a physical system showing the possibility of using SNR PUN for applying RL to real-world applications.

关键词： Training Uncertain systems computer vision Supervised learning Measurement uncertainty Reinforcement learning pattern recognition

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Accelerating Neural Field Training via Soft Mining

Accelerating Neural Field Training via Soft Mining

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shakiba Kheradmand Daniel Rebain Gopal Sharma Hossam Isack Abhishek Kar Andrea Tagliasacchi Kwang Moo Yi University of British Columbia Google Research Google DeepMind Simon Fraser University University of Toronto

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular, it is often trained by uniformly sampling the training domain, or through handcrafted heuristics. We show that improved convergence and final training quality can be achieved by a soft mining technique based on importance sampling: rather than either considering or ignoring a pixel completely, we weigh the corresponding loss by a scalar. To implement our idea we use Langevin Monte-Carlo sampling. We show that by doing so, regions with higher error are being selected more frequently, leading to more than 2x improvement in convergence speed. The code and related resources for this study are publicly available at project page.

关键词： Training computer vision Monte Carlo methods Codes Fitting Neural radiance field pattern recognition

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BMAD: Benchmarks for Medical Anomaly Detection

BMAD: Benchmarks for Medical Anomaly Detection

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Jinan Bao Hanshi Sun Hanqiu Deng Yinsheng He Zhaoxiang Zhang Xingyu Li Electrical and Computer Engineering University of Alberta Electrical and Computer Engineering Carnegie Mellon University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In the field of medical imaging, AD plays a crucial role in identifying anomalies that may indicate rare diseases or conditions. However, despite its importance, there is currently a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To address this gap, we present a comprehensive evaluation benchmark for assessing AD methods on medical images. This benchmark consists of six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fifteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables researchers to easily compare and evaluate different AD methods, and ultimately leads to the development of more effective and robust AD algorithms for medical imaging. More information on BMAD is available in our GitHub repository: https://***/DorisBao/BMAD 1

关键词： computer vision Benchmark testing Video surveillance Retina pattern recognition Medical diagnosis Medical diagnostic imaging

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A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems

A Study on the Relative Importance of Convolutional Neural N...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Deldjoo, Yashar Di Noia, Tommaso Malitesta, Daniele Merra, Felice Antonio Politecn Bari Bari Italy

ISBN: (纸本)9781665448994

Visually-aware recommender systems (VRSs) enhance the semantics of user-item interactions with visual features extracted from item images when they are available. Traditionally, VRSs leverage the representational power of pre-trained convolutional neural networks (CNNs) to perform the item recommendation task. The adoption of CNNs is mainly attributed to their outstanding performance in representing visual data for supervised learning tasks, such as image classification. Their main drawback is that the learned representation of these networks is not entirely in line with the RS tasks - learning users' preferences. This work aims to provide a better understanding of the representation power of pretrained CNNs commonly adopted by the community when integrated with state-of-the-art VRSs algorithms. In particular, we evaluate the recommendation performance of a suite of VRSs using several pretrained CNNs as the image feature extractors on two datasets from a real-world e-commerce platform. Additionally, we propose a novel qualitative and quantitative evaluation paradigm to assess the visual diversity of recommended items compared to the interacted user's items.

关键词： Visualization conferences Supervised learning Semantics Feature extraction pattern recognition Convolutional neural networks

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Driving Everywhere with Large Language Model Policy Adaptation

Driving Everywhere with Large Language Model Policy Adaptati...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Boyi Li Yue Wang Jiageng Mao Boris Ivanovic Sushant Veer Karen Leung Marco Pavone NVIDIA University of Southern California University of Washington Stanford University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Adapting driving behavior to new environments, customs, and laws is a longstanding problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs). In this paper, we present LLaDA, a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study, we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: LLaDA.

关键词： Measurement computer vision Video on demand Accuracy Large language models Planning pattern recognition

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