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共 3320 条记录，以下是2001-2010 订阅

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vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning

Vision-Language Pseudo-Labels for Single-Positive Multi-Labe...

引用

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

作者： Xin Xing Zhexiao Xiong Abby Stylianou Srikumar Sastry Liyu Gong Nathan Jacobs University of Nebraska Omaha Washington University in St. Louis Saint Louis University Oracle Inc

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We study a limited label problem and present a novel approach to Single-Positive Multi-label Learning. In the multi-label learning setting, a model learns to predict multiple labels or categories for a single input image. This contrasts with standard multi-class image classification, where the task is to predict a single label from many possible labels for an image. Single-Positive Multi-label Learning specifically considers learning to predict multiple labels when there is only one annotation per image in the training data. Multi-label learning is a more natural task than single-label learning because real-world data often involves instances belonging to multiple categories simultaneously; however, most computer vision datasets contain single labels due to the inherent complexity and cost of collecting multiple high-quality annotations per image. We propose a novel approach called vision-Language Pseudo-Labeling, which uses a vision-language model, CLIP, to suggest strong positive and negative pseudo-labels. The experiment performance shows the effectiveness of the proposed model. Our code and data will be made publicly available at https://***/mvrl/VLPL.

关键词： computer vision Costs Annotations Computational modeling conferences Training data Predictive models

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

作者： Yuka Ogino Kazuya Kakizaki Takahiro Toizumi Atsushi Ito NEC Corporation

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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： Perturbation methods Instruments Impersonation attacks Robustness pattern recognition Security Pupils

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Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation

Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupe...

引用

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

作者： Janet Wang Yunbei Zhang Zhengming Ding Jihun Hamm Tulane University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The development of reliable and fair diagnostic systems is often constrained by the scarcity of labeled data. To address this challenge, our work explores the feasibility of unsupervised domain adaptation (UDA) to integrate large external datasets for developing reliable classifiers. The adoption of UDA with multiple sources can simultaneously enrich the training set and bridge the domain gap between different skin lesion datasets, which vary due to distinct acquisition protocols. Particularly, UDA shows practical promise for improving diagnostic reliability when training with a custom skin lesion dataset, where only limited labeled data are available from the target domain. In this study, we investigate three UDA training schemes based on source data utilization: single-source, combined-source, and multi-source UDA. Our findings demonstrate the effectiveness of applying UDA on multiple sources for binary and multi-class classification. A strong correlation between test error and label shift in multi-class tasks has been observed in the experiment. Crucially, our study shows that UDA can effectively mitigate bias against minority groups and enhance fairness in diagnostic systems, while maintaining superior classification performance. This is achieved even without directly implementing fairness-focused techniques. This success is potentially attributed to the increased and well-adapted demographic information obtained from multiple sources.

关键词： Training computer vision Protocols Correlation conferences Skin pattern recognition

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Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection

Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distrib...

引用

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

作者： Syed Sha Qutub Michael Paulitsch Kay-Ulrich Scholl Neslihan Kose Cihangir Korbinian Hagn Fabian Oboril Gereon Hinz Alois Knoll Intel Labs Munich Germany Technical University of Munich Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce Situation Monitor, a novel zero-shot Out-of-Distribution (OOD) detection approach for transformer-based object detection models to enhance reliability in safety-critical machine learning applications such as autonomous driving. The Situation Monitor utilizes the Diversity-based Budding Ensemble Architecture (DBEA) and increases the OOD performance by integrating a diversity loss into the training process on top of the budding ensemble architecture, detecting Far-OOD samples and minimizing false positives on Near-OOD samples. Moreover, utilizing the resulting DBEA increases the model’s OOD performance and improves the calibration of confidence scores, particularly concerning the intersection over union of the detected objects. The DBEA model achieves these advancements with a 14% reduction in trainable parameters compared to the vanilla model. This signifies a substantial improvement in efficiency without compromising the model’s ability to detect OOD instances and calibrate the confidence scores accurately.

关键词： Training Adaptation models Zero-shot learning Computational modeling Scalability computer architecture Object detection

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Photo-Realistic Image Restoration in the Wild with Controlled vision-Language Models

Photo-Realistic Image Restoration in the Wild with Controlle...

引用

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

作者： Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Uppsala University Karolinska Institutet

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets. Typically, diffusion models trained in specific datasets fail to recover images that have out-of-distribution degradations. To address this problem, this work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild (wild IR). More specifically, all low-quality images are simulated with a synthetic degradation pipeline that contains multiple common degradations such as blur, resize, noise, and JPEG compression. Then we introduce robust training for a degradation-aware CLIP model to extract enriched image content features to assist high-quality image restoration. Our base diffusion model is the image restoration SDE (IR-SDE). Built upon it, we further present a posterior sampling strategy for fast noise-free image generation. We evaluate our model on both synthetic and real-world degradation datasets. Moreover, experiments on the unified image restoration task illustrate that the proposed posterior sampling improves image generation quality for various degradations.

关键词： Degradation Training Image synthesis Pipelines Transform coding Diffusion models Feature extraction

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MixSyn: Compositional Image Synthesis with Fuzzy Masks and Style Fusion

MixSyn: Compositional Image Synthesis with Fuzzy Masks and S...

引用

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

作者： İlke Demir Umur Aybars Çiftçi Intel Labs Binghamton University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic images created by generative models increase in quality and expressiveness as newer models utilize larger datasets and novel architectures. Although this photorealism is a benefit from a creative standpoint, expressiveness is still limited by the training data. Most of these approaches are built on the transfer between source and target pairs, or they generate completely new samples based on an ideal distribution, still resembling the closest real sample while missing less frequent or non-existent compositions. We propose MixSyn (read as " mixin’ ") to learn novel fuzzy compositions from multiple sources and to create novel images as a mix of image regions corresponding to the compositions. MixSyn not only combines uncorrelated regions from multiple source masks into a coherent semantic composition, but also generates mask-aware high quality reconstructions of non-existing images. We compare MixSyn to state-of-the-art single-source sequential generation and collage generation approaches in terms of quality, diversity, realism, and expressive power; comparing region-wise reconstruction and similarity scores. We also showcase interactive synthesis, mix & match, design space exploration, and edit propagation tasks, with no mask dependency.

关键词： Photorealism computer vision Image synthesis conferences Semantics Training data computer architecture

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Rugby Scene Classification Enhanced by vision Language Model

Rugby Scene Classification Enhanced by Vision Language Model

引用

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

作者： Naoki Nonaka Ryo Fujihira Toshiki Koshiba Akira Maeda Jun Seita Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters Hakata Knee & Sports Clinic

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study investigates the integration of vision language models (VLM) to enhance the classification of situations within rugby match broadcasts. The importance of accurately identifying situations in sports videos is emphasized for understanding game dynamics and facilitating downstream tasks like performance evaluation and injury prevention. Utilizing a dataset comprising 18, 000 labeled images extracted at 0.2-second intervals from 100 minutes of rugby match broadcasts, scene classification tasks including contact plays (scrums, mauls, rucks, tackles, lineouts), rucks, tackles, lineouts, and multiclass classification were performed. The study aims to validate the utility of VLM outputs in improving classification performance compared to using solely image data. Experimental results demonstrate substantial performance improvements across all tasks with the incorporation of VLM outputs. Our analysis of prompts suggests that, when provided with appropriate contextual information through natural language, VLMs can effectively capture the context of a given image. The findings of our study indicate that leveraging VLMs in the domain of sports analysis holds promise for developing image processing models capable of incorpolating the tacit knowledge encoded within language models, as well as information conveyed through natural language descriptions.

关键词： Performance evaluation Scene classification Analytical models Computational modeling Prevention and mitigation Natural languages pattern recognition

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Localised-NeRF: Specular Highlights and Colour Gradient Localising in NeRF

Localised-NeRF: Specular Highlights and Colour Gradient Loca...

引用

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

作者： Dharmendra Selvaratnam Dena Bazazian Faculty of Science and Engineering School of Engineering Computing and Mathematics (SECaM) University of Plymouth

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Field (NeRF) based systems predominantly operate within the RGB (Red, Green, and Blue) space; however, the distinctive capability of the HSV (Hue, Saturation, and Value) space to discern between specular and diffuse regions is seldom utilised in the literature. We introduce Localised-NeRF, which projects the queried pixel point onto multiple training images to obtain a multi-view feature representation on HSV space and gradient space to obtain important features that can be used to synthesise novel view colour. This integration is pivotal in identifying specular highlights within scenes, thereby enriching the model’s understanding of specular changes as the viewing angle alters. Our proposed Localised-NeRF model uses an attention-driven approach that not only maintains local view direction consistency but also leverages image-based features namely the HSV colour space and colour gradients. These features serve as effective indirect priors for both the training and testing phases to predict the diffuse and specular colour. Our model exhibits competitive performance with prior NeRF-based models, as demonstrated on the Shiny Blender and Synthetic datasets. The code of Localised-NeRF is publicly available 1 .

关键词： Training computer vision Visualization Image color analysis Neural radiance field Rendering (computer graphics) Mathematical models

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Can the accuracy bias by facial hairstyle be reduced through balancing the training data?

Can the accuracy bias by facial hairstyle be reduced through...

引用

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

作者： Kagan Ozturk Haiyu Wu Kevin W. Bowyer University of Notre Dame

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Appearance of a face can be greatly altered by growing a beard and mustache. The facial hairstyles in a pair of images can cause marked changes to the impostor distribution and the genuine distribution. Also, different distributions of facial hairstyle across demographics could cause a false impression of relative accuracy across demographics. We first show that, even though larger training sets boost the recognition accuracy on all facial hairstyles, accuracy variations caused by facial hairstyles persist regardless of the size of the training set. Then, we analyze the impact of having different fractions of the training data represent facial hairstyles. We created balanced training sets using a set of identities available in Webface42M that both have clean-shaven and facial hair images. We find that, even when a face recognition model is trained with a balanced clean-shaven / facial hair training set, accuracy variation on the test data does not diminish. Next, data augmentation is employed to further investigate the effect of facial hair distribution in training data by manipulating facial hair pixels with the help of facial landmark points and a facial hair segmentation model. Our results show facial hair causes an accuracy gap between clean-shaven and facial hair images, and this impact can be significantly different between African-Americans and Caucasians.

关键词： Hair Training Image segmentation computer vision Accuracy Face recognition conferences

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Data-Efficient and Robust Task Selection for Meta-Learning

Data-Efficient and Robust Task Selection for Meta-Learning

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

作者： Donglin Zhan James Anderson Columbia University New York NY

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Meta-learning methods typically learn tasks under the assumption that all tasks are equally important. However, this assumption is often not valid. In real-world applications, tasks can vary both in their importance during different training stages and in whether they contain noisy labeled data or not, making a uniform approach suboptimal. To address these issues, we propose the Data-Efficient and Robust Task Selection (DERTS) algorithm, which can be incorporated into both gradient and metric-based meta-learning algorithms. DERTS selects weighted subsets of tasks from task pools by minimizing the approximation error of the full gradient of task pools in the meta-training stage. The selected tasks are efficient for rapid training and robust towards noisy label scenarios. Unlike existing algorithms, DERTS does not require any architecture modification for training and can handle noisy label data in both the support and query sets. Analysis of DERTS shows that the algorithm follows similar training dynamics as learning on the full task pools. Experiments show that DERTS outperforms existing sampling strategies for meta-learning on both gradient-based and metric-based meta-learning algorithms in limited data budget and noisy task settings.

关键词： Metalearning Training computer vision Heuristic algorithms conferences computer architecture Approximation algorithms

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