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Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models

Generation of Structurally Realistic Retinal Fundus Images w...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sojung Go Younghoon Ji Sang Jun Park Soochahn Lee Department of Ophthalmology Seoul National University College of Medicine Seoul National University Bundang Hospital Seongnam Korea VUNO Inc. Seoul Korea School of Electrical Engineering Kookmin University Seoul Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a new technique for generating retinal fundus images that have anatomically accurate vascular structures, using diffusion models. We generate artery/vein masks to create the vascular structure, which we then condition to produce retinal fundus images. The proposed method can generate high-quality images with more realistic vascular structures and can create a diverse range of images based on the strengths of the diffusion model. We present quantitative evaluations that demonstrate the performance improvement using our method for data augmentation on vessel segmentation and artery/vein classification. We also present Turing test results by clinical experts, showing that our generated images are difficult to distinguish with real images. We believe that our method can be applied to construct stand-alone datasets that are irrelevant of patient privacy.

关键词： Image segmentation Data privacy computer vision Accuracy conferences Retina Diffusion models

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EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction

EVREAL: Towards a Comprehensive Benchmark and Analysis Suite...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Burak Ercan Onur Eker Aykut Erdem Erkut Erdem Computer Engineering Department Hacettepe University HAVELSAN Inc Computer Engineering Department Koç University KUIS AI Center Koç University

Event cameras are a new type of vision sensor that incorporates asynchronous and independent pixels, offering advantages over traditional frame-based cameras such as high dynamic range and minimal motion blur. However, their output is not easily understandable by humans, making the reconstruction of intensity images from event streams a fundamental task in event-based vision. While recent deep learning-based methods have shown promise in video reconstruction from events, this problem is not completely solved yet. To facilitate comparison between different approaches, standardized evaluation protocols and diverse test datasets are essential. This paper proposes a unified evaluation methodology and introduces an open-source framework called EVREAL to comprehensively benchmark and analyze various event-based video reconstruction methods from the literature. Using EVREAL, we give a detailed analysis of the state-of-the-art methods for event-based video reconstruction, and provide valuable insights into the performance of these methods under varying settings, challenging scenarios, and downstream tasks.

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Thermal Image Super-Resolution Challenge Results - PBVS 2024

Thermal Image Super-Resolution Challenge Results - PBVS 2024

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Rafael E. Rivadeneira Angel D. Sappa Chenyang Wang Junjun Jiang Zhiwei Zhong Peilin Chen Shiqi Wang Escuela Superior Politécnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper outlines the advancements and results of the Fifth Thermal Image Super-Resolution challenge, hosted at the Perception Beyond the Visible Spectrum CVPR 2024 workshop. The challenge employed a novel benchmark cross-spectral dataset consisting of 1000 thermal images, each paired with its corresponding registered RGB image. The challenge featured two tracks: Track-1 focused on Single Thermal Image Super-Resolution with an ×8 upscale factor, while Track-2 extended its evaluation to include both ×8 and ×16 scaling factors, utilizing high-resolution RGB images to guide the super-resolution process for low-resolution thermal images. The participation of over 175 teams highlights the research community’s strong engagement and dedication to enhancing image resolution techniques across both single and cross-spectral methodologies. This year’s challenge sets new benchmarks and provides valuable insights into future directions for research in thermal image super-resolution.

关键词： Image quality computer vision Thermal factors conferences Superresolution Benchmark testing Transformers

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FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer

FedProK: Trustworthy Federated Class-Incremental Learning vi...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xin Gao Xin Yang Hao Yu Yan Kang Tianrui Li Southwestern University of Finance and Economics Webank Southwest Jiaotong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing methods do not consider the trustworthiness of FCIL, i.e., improving continual utility, privacy, and efficiency simultaneously, which is greatly influenced by catastrophic forgetting and data heterogeneity among clients. To address this issue, we propose FedProK (Federated Prototypical Feature Knowledge Transfer), leveraging prototypical feature as a novel representation of knowledge to perform spatial-temporal knowledge transfer. Specifically, FedProK consists of two components: (1) feature translation procedure on the client side by temporal knowledge transfer from the learned classes and (2) prototypical knowledge fusion on the server side by spatial knowledge transfer among clients. Extensive experiments conducted in both synchronous and asynchronous settings demonstrate that our FedProK outperforms the other state-of-the-art methods in three perspectives of trustworthiness, validating its effectiveness in selectively transferring spatial-temporal knowledge.

关键词： Privacy Data privacy computer vision Federated learning conferences Prototypes Benchmark testing

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InViG: Benchmarking Open-Ended Interactive Visual Grounding with 500K Dialogues

InViG: Benchmarking Open-Ended Interactive Visual Grounding ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hanbo Zhang Jie Xu Yuchen Mo Tao Kong ByteDance Research Beijing China Xi’an Jiaotong University Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Ambiguity is ubiquitous in human communication. Previous approaches in Human-Robot Interaction (HRI) have often relied on predefined interaction templates, leading to reduced performance in realistic and open-ended scenarios. To address these issues, we present a large-scale dataset, InViG, for interactive visual grounding under language ambiguity. Our dataset comprises over 520K images accompanied by open-ended goal-oriented disambiguation dialogues, encompassing millions of object instances and corresponding question-answer pairs. Leveraging the InViG dataset, we conduct extensive studies and propose a set of baseline solutions for end-to-end interactive visual disambiguation and grounding, achieving a 45.6% success rate during validation. To the best of our knowledge, the InViG dataset is the first large-scale dataset for resolving open-ended interactive visual grounding, presenting a practical yet highly challenging benchmark for ambiguity-aware HRI. Codes and datasets are available at: https://***.

关键词： Visualization computer vision Image resolution Codes Grounding conferences Human-robot interaction

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Practical Region-level Attack against Segment Anything Models

Practical Region-level Attack against Segment Anything Model...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yifan Shen Zhengyuan Li Gang Wang University of Illinois Urbana-Champaign

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Segment Anything Models (SAM) have made significant advancements in image segmentation, allowing users to segment target portions of an image with a single click (i.e., user prompt). Given its broad applications, the robustness of SAM against adversarial attacks is a critical concern. While recent works have explored adversarial attacks against a pre-defined prompt/click, their threat model is not yet realistic: (1) they often assume the user-click position is known to the attacker (point-based attack), and (2) they often operate under a white-box setting with limited transferability. In this paper, we propose a more practical region-level attack where attackers do not need to know the precise user prompt. The attack remains effective as the user clicks on any point on the target object in the image, hiding the object from SAM. Also, by adapting a spectrum transformation method, we make the attack more transferable under a black-box setting. Both control experiments and testing against real-world SAM services confirm its effectiveness.

关键词： Threat modeling Image segmentation computer vision conferences Computational modeling Closed box Robustness

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FineRehab: A Multi-modality and Multi-task Dataset for Rehabilitation Analysis

FineRehab: A Multi-modality and Multi-task Dataset for Rehab...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jianwei Li Jun Xue Rui Cao Xiaoxia Du Siyu Mo Kehao Ran Zeyan Zhang School of Sports Engineering Beijing Sport University China Department of Neurorehabilitation Rehabilitation Research Center China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The assessment of rehabilitation exercises for neurological and musculoskeletal disorders are crucial for recovery. Traditionally, assessment methods have been subjective, with inherent uncertainty and limitations. This paper introduces a novel multi-modality dataset named FineRehab § to prompt the study of rehabilitation movement analysis, leveraging advancements in sensor technology and artificial intelligence. FineRehab collects 16 actions from 50 participants, including both patients with musculoskeletal disorders and healthy individuals, and consists of 4,215 action samples captured by two Kinect cameras and 17 IMUs. To benchmark FineRehab, we present a reliable approach to analyze rehabilitation exercises, and make experiments to evaluate the comprehensive movement quality from across multi-dimensions. Comparative experimental analyses have verified the validity of our dataset in distinguishing between the movement of the normal population and patients, which can offer a quantifiable basis for personalized rehabilitation feedback. The introduction of FineRehab will encourage researchers to apply, develop and adapt various methods for rehabilitation exercise analysis.

关键词： Deep learning Musculoskeletal system computer vision Uncertainty conferences Multitasking Cameras

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Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images

Depth-Regularized Optimization for 3D Gaussian Splatting in ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jaeyoung Chung Jeongtaek Oh Kyoung Mu Lee Department of ECE ASRI IPAI Seoul National University Seoul Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a few images are available. To address this issue, we employ an adjusted depth map as a geometric reference, derived from a pre-trained monocular depth estimation model and subsequently aligned with the sparse structure-from-motion points. We regularize the optimization process of 3D Gaussian splatting with the adjusted depth and an additional unsupervised smooth constraint, thereby effectively reducing the occurrence of floating artifacts. Our method is mainly validated on the NeRF-LLFF dataset with varying numbers of images, and we conduct multiple experiments with randomly selected training images, presenting the average value to ensure fairness. Our approach demonstrates robust geometry compared to the original method, which relied solely on images.

关键词： Training Geometry Visualization Solid modeling computer vision Three-dimensional displays conferences

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Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models

Investigating the Effectiveness of Cross-Attention to Unlock...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Saman Motamed Wouter Van Gansbeke Luc Van Gool INSAIT Sofia University Bulgaria ETH Zurich KU Leuven

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, manipulating the cross-attention layers of Text-to-Image (T2I) diffusion models has shown great promise in controlling the shape and location of objects in the scene. Transferring image-editing techniques to the video domain, however, is extremely challenging as object motion and temporal consistency are difficult to capture accurately. In this work, we take a first look at the role of cross-attention in Text-to-Video (T2V) diffusion models for zero-shot video editing. While one-shot models have shown potential in controlling motion and camera movement, we demonstrate zero-shot control over object shape, position and movement in T2V models. We show that despite the limitations of current T2V models, cross-attention guidance can be a promising approach for editing videos. Code: https://***/sam-motamed/***

关键词： computer vision Codes Shape Computational modeling conferences Text to image Diffusion models

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Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection

Cross-Modal Fusion and Attention Mechanism for Weakly Superv...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ayush Ghadiya Purbayan Kar Vishal Chudasama Pankaj Wasnik Media Analysis Group Sony Research India Bangalore India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, weakly supervised video anomaly detection (WS-VAD) has emerged as a contemporary research direction to identify anomaly events like violence and nudity in videos using only video-level labels. However, this task has substantial challenges, including addressing imbalanced modality information and consistently distinguishing between normal and abnormal features. In this paper, we address these challenges and propose a multi-modal WS-VAD framework to accurately detect anomalies such as violence and nudity. Within the proposed framework, we introduce a new fusion mechanism known as the Cross-modal Fusion Adapter (CFA), which dynamically selects and enhances highly relevant audio-visual features in relation to the visual modality. Additionally, we introduce a Hyperbolic Lorentzian Graph Attention (HLGAtt) to effectively capture the hierarchical relationships between normal and abnormal representations, thereby enhancing feature separation accuracy. Through extensive experiments, we demonstrate that the proposed model achieves state-of-the-art results on benchmark datasets of violence and nudity detection.

关键词： Visualization computer vision Adaptation models Accuracy Attention mechanisms conferences Computational modeling

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