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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

来源：评论

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MP-PolarMask: A Faster and Finer Instance Segmentation for Concave Images

MP-PolarMask: A Faster and Finer Instance Segmentation for C...

引用

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

作者： Ke-Lei Wang Pin-Hsuan Chou Young-Ching Chou Chia-Jen Liu Cheng-Kuan Lin Yu-Chee Tseng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Emergency and Critical Care Medicine National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While there are a lot of models for instance segmentation, PolarMask stands out as a unique one that represents an object by a Polar coordinate system. With an anchor-box-free design and a single-stage framework that conducts detection and segmentation at one time, PolarMask is proved to be able to balance efficiency and accuracy. Hence, it can be easily connected with other downstream real-time applications. In this work, we observe that there are two deficiencies associated with PolarMask: (i) inability of representing concave objects and (ii) inefficiency in using ray regression. We propose MP-PolarMask (Multi-Point PolarMask) by taking advantage of multiple Polar systems. The main idea is to extend from one main Polar system to four auxiliary Polar systems, thus capable of representing more complicated convex-and-concave-mixed shapes. We validate MP-PolarMask on both general objects and food objects of the COCO dataset, and the results demonstrate significant improvement of 13.69% in AP L and 7.23% in AP over PolarMask with 36 rays.

关键词： Instance segmentation computer vision Accuracy Shape conferences Real-time systems pattern recognition

来源：评论

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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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LatentMan : Generating Consistent Animated Characters using Image Diffusion Models

LatentMan : Generating Consistent Animated Characters using ...

引用

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

作者： Abdelrahman Eldesokey Peter Wonka KAUST Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models. Existing Text-to-Video (T2V) methods are expensive to train and require large-scale video datasets to produce diverse characters and motions. At the same time, their zero-shot alternatives fail to produce temporally consistent videos with continuous motion. We strive to bridge this gap, and we introduce LATENTMAN that leverages existing text-based motion diffusion models to generate diverse continuous motions to guide the T2I model. To boost the temporal consistency, we introduce the Spatial Latent Alignment module that exploits cross-frame dense correspondences that we compute to align the latents of the video frames. Furthermore, we propose Pixel-Wise Guidance to steer the diffusion process in a direction that minimizes visual discrepancies between frames. Our proposed approach outperforms existing zero-shot T2V approaches in generating videos of animated characters in terms of pixel-wise consistency and user preference.

关键词： Bridges Visualization computer vision conferences Computational modeling Text to image Diffusion processes

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

引用

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

作者： Anshul Gupta Pierre Vuillecard Arya Farkhondeh Jean-Marc Odobez Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Contextual cues related to a person’s pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Visualization computer vision conferences Computational modeling Predictive models pattern recognition Context modeling

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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...

引用

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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How SAM Perceives Different mp-MRI Brain Tumor Domains?

How SAM Perceives Different mp-MRI Brain Tumor Domains?

引用

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

作者： Cecilia Diana-Albelda Roberto Alcover-Couso Álvaro García-Martín Jesus Bescos Universidad Autónoma de Madrid

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Gliomas, among the deadliest forms of cancer, are brain tumors that present a significant challenge due to their rapid progression and resistance to treatment. Effective and early diagnosis is critical for improving patient prognosis. Deep learning, particularly through large-scale vision models like Segment Anything Model (SAM), offers a new pathway for tumor segmentation. This study seeks to address the primary challenge of adapting SAM for mp-MRI brain scans, which typically encompass multiple imaging modalities not fully utilized by standard three-channel vision models. We demonstrate that leveraging all available MRI modalities achieves superior performance compared to the standard mechanism of repeating a MRI scan to fit the input embedding. Our research also focuses on parameter-efficient tuning of SAM to effectively train the model while minimizing resource usage, showcasing significant improvements when evaluated across multiple datasets. Finally, we expose how SAM perceives differences across varied brain tumor domains by visually analyzing the features extracted on each of them. Our code and models are available at ***/vpulab/med-sam-brain.

关键词： Adaptation models Image segmentation Magnetic resonance imaging Feature extraction Brain modeling pattern recognition Prognostics and health management

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FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model...

引用

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

作者： Julia Yang Alina Jade Barnett Jon Donnelly Satvik Kishore Jerry Fang Fides Regina Schwartz Chaofan Chen Joseph Y. Lo Cynthia Rudin Duke University Durham NC USA Brigham and Women’s Hospital Boston MA USA University of Maine Orono ME USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.

关键词： Deep learning computer vision Accuracy Computational modeling Prototypes computer architecture Radiology

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Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models

Audio-Visual Generalized Zero-Shot Learning using Pre-Traine...

引用

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

作者： David Kurzendörfer Otniel-Bogdan Mercea A. Sophia Koepke Zeynep Akata University of Tübingen Localyzer GmbH Tübingen AI Center Helmholtz Munich Technical University of Munich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Audio-visual zero-shot learning methods commonly build on features extracted from pre-trained models, e.g. video or audio classification models. However, existing benchmarks predate the popularization of large multi-modal models, such as CLIP and CLAP. In this work, we explore such large pre-trained models to obtain features, i.e. CLIP for visual features, and CLAP for audio features. Furthermore, the CLIP and CLAP text encoders provide class label embeddings which are combined to boost the performance of the system. We propose a simple yet effective model that only relies on feed-forward neural networks, exploiting the strong generalization capabilities of the new audio, visual and textual features. Our framework achieves state-of-the-art performance on VGGSound-GZSL cls , UCF-GZSL cls , and ActivityNet-GZSL cls with our new features. Code and data available at: https://***/dkurzend/ClipClap-GZSL.

关键词： Visualization computer vision Codes Zero-shot learning conferences Computational modeling Benchmark testing

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Exploring the Impact of Dataset Bias on Dataset Distillation

Exploring the Impact of Dataset Bias on Dataset Distillation

引用

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

作者： Yao Lu Jianyang Gu Xuguang Chen Saeed Vahidian Qi Xuan Zhejiang University of Technology Zhejiang University Duke University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that preserves essential information from the original dataset. This synthetic dataset can serve as a substitute for the original large-scale one, and help alleviate the training workload. However, current DD methods typically operate under the assumption that the dataset is unbiased, overlooking potential bias issues within the dataset itself. To fill in this blank, we systematically investigate the influence of dataset bias on DD. Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis. Then we utilize existing DD methods to generate synthetic datasets on CMNIST-DD and CCIFAR10-DD, and evaluate their performance following the standard process. Experiments demonstrate that biases present in the original dataset significantly impact the performance of the synthetic dataset in most cases, which highlights the necessity of identifying and mitigating biases in the original datasets during DD. Finally, we reformulate DD within the context of a biased dataset. Our code along with biased datasets are available at https://***/yaolu-zjut/Biased-DD.

关键词： Training computer vision Codes Prevention and mitigation conferences Mathematical models pattern recognition

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