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ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization

ConPro: Learning Severity Representation for Medical Images ...

引用

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

作者： Hong Nguyen Hoang Nguyen Melinda Chang Hieu Pham Shrikanth Narayanan Michael Pazzani University of Southern California Los Angeles United States Vinuni-Illinois Smart Health Center Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding the severity of conditions shown in images in medical diagnosis is crucial, serving as a key guide for clinical assessment, treatment, as well as evaluating longitudinal progression. This paper proposes ConPrO: a novel representation learning method for severity assessment in medical images using Contrastive learning-integrated Preference Optimization. Different from conventional contrastive learning methods that maximize the distance between classes, ConPrO injects into the latent vector the distance preference knowledge between various severity classes and the normal class. We systematically examine the key components of our framework to illuminate how contrastive prediction tasks acquire valuable representations. We show that our representation learning framework offers valuable severity ordering in the feature space while outperforming previous state-of-the-art methods on classification tasks. We achieve a 6% and 20% relative improvement compared to a supervised and a self-supervised baseline, respectively. In addition, we derived discussions on severity indicators and related applications of preference comparison in the medical domain.

关键词： Representation learning Measurement Visualization Contrastive learning Prediction algorithms Vectors pattern recognition

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Sparse multi-view hand-object reconstruction for unseen environments

Sparse multi-view hand-object reconstruction for unseen envi...

引用

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

作者： Yik Lung Pang Changjae Oh Andrea Cavallaro Centre for Intelligent Sensing Queen Mary University of London Idiap Research Institute École Polytechnique Fédérale de Lausanne

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent works in hand-object reconstruction mainly focus on the single-view and dense multi-view settings. On the one hand, single-view methods can leverage learned shape priors to generalise to unseen objects but are prone to inaccuracies due to occlusions. On the other hand, dense multi-view methods are very accurate but cannot easily adapt to unseen objects without further data collection. In contrast, sparse multi-view methods can take advantage of the additional views to tackle occlusion, while keeping the computational cost low compared to dense multi-view methods. In this paper, we consider the problem of hand-object reconstruction with unseen objects in the sparse multi-view setting. Given multiple RGB images of the hand and object captured at the same time, our model SVHO combines the predictions from each view into a unified reconstruction without optimisation across views. We train our model on a synthetic hand-object dataset and evaluate directly on a real world recorded hand-object dataset with unseen objects. We show that while reconstruction of unseen hands and objects from RGB is challenging, additional views can help improve the reconstruction quality.

关键词： computer vision Shape Computational modeling conferences Predictive models Data collection pattern recognition

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FairSSD: Understanding Bias in Synthetic Speech Detectors

FairSSD: Understanding Bias in Synthetic Speech Detectors

引用

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

作者： Amit Kumar Singh Yadav Kratika Bhagtani Davide Salvi Paolo Bestagini Edward J. Delp Video and Image Processing Lab (VIPER) Purdue University West Lafayette Indiana USA Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Methods that can generate synthetic speech which is perceptually indistinguishable from speech recorded by a human speaker, are easily available. Several incidents report misuse of synthetic speech generated from these methods to commit fraud. To counter such misuse, many methods have been proposed to detect synthetic speech. Some of these detectors are more interpretable, can generalize to detect synthetic speech in the wild and are robust to noise. However, limited work has been done on understanding bias in these detectors. In this work, we examine bias in existing synthetic speech detectors to determine if they will unfairly target a particular gender, age and accent group. We also inspect whether these detectors will have a higher misclassification rate for bona fide speech from speech-impaired speakers w.r.t fluent speakers. Extensive experiments on 6 existing synthetic speech detectors using more than 0.9 million speech signals demonstrate that most detectors are gender, age and accent biased, and future work is needed to ensure fairness. To support future research, we release our evaluation dataset, models used in our study and source code at https://***/viper-purdue/fairssd.

关键词： computer vision Source coding conferences Noise Detectors pattern recognition Fraud

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Dformer: Learning Efficient Image Restoration with Perceptual Guidance

Dformer: Learning Efficient Image Restoration with Perceptua...

引用

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

作者： Nodirkhuja Khudjaev Roman Tsoy S M A Sharif Azamat Myrzabekov Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image restoration tasks incorporate widespread real-world application. Apart from its significant practicability, generic deep image restoration methods still fail to handle complex tasks, like shadow removal, low-light enhancement, etc. This paper addresses the limitations of existing image restoration methods by introducing a novel deep architecture. The proposed model incorporates illumination mapping inspired by the Retinex theory within a double encoder-decoder network. Additionally, it utilizes a multi-head cross-attention mechanism to correlate input and reconstructed images to generate plausible and refined images. The proposed model employs a perceptual optimization strategy to tackle intricate restoration tasks effectively. It surpasses state-of-the-art methods in demanding tasks such as shadow removal, low-light image enhancement, and blind compress image enhancement, all while utilizing fewer trainable parameters. Our method is selected among the top solutions in the New Trends in Image Restoration and Enhancement’24 (NTIRE) challenge for shadow removal, securing a top position without resorting to score-boosting techniques such as ensembling.

关键词： Image coding Image edge detection Lighting Deep architecture Market research Image restoration pattern recognition

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Multi-View Body Image-Based Prediction of Body Mass Index and Various Body Part Sizes

Multi-View Body Image-Based Prediction of Body Mass Index an...

引用

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

作者： Seunghyun Kim Kunyoung Lee Eui Chul Lee Department of AI & Informatics Graduate School Sangmyung University Department of Computer Science Graduate School Sangmyung University Department of Human-Centered Artificial Intelligence Sangmyung University

This paper proposes a novel model for predicting body mass index and various body part sizes using front, side, and back body images. The model is trained on a large dataset of labeled images. The results show that the model can accurately predict body mass index and various body part sizes such as chest, waist, hip, thigh, forearm, and shoulder width. One significant advantage of the proposed model is that it can use multiple views of the body to achieve more accurate predictions, overcoming the limitations of models that only used a single image. The model also does not require complex pre-processing or feature extraction, making it straightforward to apply in practice. We also explore the impact of different environmental factors, such as clothing and posture, on the model's performance. The findings show that the model is relatively insensitive to posture but is more sensitive to clothing, emphasizing the importance of controlling for clothing when using this model. Overall, the proposed model represents a step forward in predicting body mass index and various body part sizes from images. The model's accuracy, convenience, and ability to use multiple views of the body make it a promising tool for a wide range of applications. The proposed method is expected to be utilized as a parameter for accurate sensing of various vision-based non-contact biomarkers, in addition to body mass index inference.

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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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Red-Teaming Segment Anything Model

Red-Teaming Segment Anything Model

引用

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

作者： Krzysztof Jankowski Bartlomiej Sobieski Mateusz Kwiatkowski Jakub Szulc Michał Janik Hubert Baniecki Przemysław Biecek University of Warsaw Warsaw University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests the Segment Anything Model against challenging tasks: (1) We analyze the impact of style transfer on segmentation masks, demonstrating that applying adverse weather conditions and raindrops to dashboard images of city roads significantly distorts generated masks. (2) We focus on assessing whether the model can be used for attacks on privacy, such as recognizing celebrities’ faces, and show that the model possesses some undesired knowledge in this task. (3) Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts. We not only show the effectiveness of popular white-box attacks and resistance to black-box attacks but also introduce a novel approach - Focused Iterative Gradient Attack (FIGA) that combines white-box approaches to construct an efficient attack resulting in a smaller number of modified pixels. All of our testing methods and analyses indicate a need for enhanced safety measures in foundation models for image segmentation.

关键词： Image segmentation Analytical models Privacy computer vision Text recognition Computational modeling Urban areas

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Leveraging Large Language Models for Multimodal Search

Leveraging Large Language Models for Multimodal Search

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

作者： Oriol Barbany Michael Huang Xinliang Zhu Arnab Dhua CSIC-UPC Institut de Robòtica i Informàtica Industrial Visual Search & AR Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal search has become increasingly important in providing users with a natural and effective way to express their search intentions. Images offer fine-grained details of the desired products, while text allows for easily incorporating search modifications. However, some existing multimodal search systems are unreliable and fail to address simple queries. The problem becomes harder with the large variability of natural language text queries, which may contain ambiguous, implicit, and irrelevant information. Addressing these issues may require systems with enhanced matching capabilities, reasoning abilities, and context-aware query parsing and rewriting. This paper introduces a novel multimodal search model that achieves a new performance milestone on the Fashion200K dataset [19]. Additionally, we propose a novel search interface integrating Large Language Models (LLMs) to facilitate natural language interaction. This interface routes queries to search systems while conversationally engaging with users and considering previous searches. When coupled with our multimodal search model, it heralds a new era of shopping assistants capable of offering human-like interaction and enhancing the overall search experience.

关键词： computer vision Large language models conferences Natural languages Pipelines Image retrieval computer architecture

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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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