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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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Improving Deep Learning-based Automatic Checkout System Using Image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

引用

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

作者： Long Hoang Pham Duong Nguyen-Ngoc Tran Huy-Hung Nguyen Hyung-Joon Jeon Tai Huu-Phuong Tran Hyung-Min Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University South Korea

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest; (2) tracking objects in consecutive frames; and (3) counting objects using a track management pipeline. Several data generation techniques—including copy-and-paste, random placement, and augmentation—are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792.

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SrvfRegNet: Elastic Function Registration Using Deep Neural Networks

SrvfRegNet: Elastic Function Registration Using Deep Neural ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Chao Srivastava, Anuj Florida State Univ Dept Stat Tallahassee FL 32306 USA

ISBN: (纸本)9781665448994

Registering functions (curves) using time warpings (re-parameterizations) is central to many computer vision and shape analysis solutions. While traditional registration methods minimize penalized-L-2 norm, the elastic Riemannian metric and square-root velocity functions (SRVFs) have resulted in significant improvements in terms of theory and practical performance. This solution uses the dynamic programming algorithm to minimize the L-2 norm between SRVFs of given functions. However, the computational cost of this elastic dynamic programming framework - O(nT(2)k) - where T is the number of time samples along curves, n is the number of curves, and k < T is a parameter - limits its use in applications involving big data. This paper introduces a deep-learning approach, named SRVF Registration Net or SrvfRegNet to overcome these limitations. SrvfRegNet architecture trains by optimizing the elastic metric-based objective function on the training data and then applies this trained network to the test data to perform fast registration. In case the training and the test data are from different classes, it generalizes to the test data using transfer learning, i.e., retraining of only the last few layers of the network. It achieves the state-of-the-art alignment performance albeit at much reduced computational cost. We demonstrate the efficiency and efficacy of this framework using several standard curve datasets.

关键词： Training Measurement computer vision Shape Transfer learning Training data Dynamic programming

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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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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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Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment

Cali-NCE: Boosting Cross-modal Video Representation Learning...

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

作者： Nanxuan Zhao Jianbo Jiao Weidi Xie Dahua Lin Department of Computer Science University of Bath School of Computer Science University of Birmingham Cooperative Medianet Innovation Center Shanghai Jiaotong University Department of Information Engineering Chinese University of Hong Kong

With the large-scale video-text datasets being collected, learning general visual-textual representation has gained increasing attention. While recent methods are designed with the assumption that the alt-text description naturally conveys the meaning and context of the video in semantics (i.e. well aligned with each other), it is unlikely to be satisfied for the Internet data, which potentially harms the quality of the learned visual-textual representation. To address this challenge, we first revisit three mainstream approaches: correspondence modeling, contrastive learning and predictive coding, demonstrating that a simple co-training strategy with these methods leads to a clear improvement in performance. To further explore the complementary nature of different training strategies, we propose a simple yet effective joint training framework that factorizes the total objective into conditional ones, termed as Cali-NCE 1 . Our method first estimates confidence scores for measuring the correspondence between video and text descriptions, and the scores are later used to calibrate the sample weightings during contrastive training. Through extensive experiments, we show that the proposed approach achieves state-of-the-art performance on multiple downstream tasks: text-to-video retrieval, video action recognition, and video retrieval.

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EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching

EMScore: Evaluating Video Captioning via Coarse-Grained and ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shi, Yaya Yang, Xu Xu, Haiyang Yuan, Chunfeng Li, Bing Hu, Weiming Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Southeast Univ Nanjing Peoples R China Alibaba Grp Beijing Peoples R China Chinese Acad Sci NLPR Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. However, they have some insuperable drawbacks, e.g., they cannot handle videos without references, and they may result in biased evaluation due to the one-to-many nature of video-to-text and the neglect of visual relevance. From the human evaluator's viewpoint, a high-quality caption should be consistent with the provided video, but not necessarily be similar to the reference in literal or semantics. Inspired by human evaluation, we propose EMScore (Embedding Matching-based score), a novel reference-free metric for video captioning, which directly measures similarity between video and candidate captions. Benefiting from the recent development of large-scale pre-training models, we exploit a well pre-trained vision-language model to extract visual and linguistic embeddings for computing EMScore. Specifically, EMScore combines matching scores of both coarse-grained (video and caption) and fine-grained (frames and words) levels, which takes the overall understanding and detailed characteristics of the video into account. Furthermore, considering the potential information gain, EMScore can be flexibly extended to the conditions where human-labeled references are available. Last but not least, we collect VATEX-EVAL and ActivityNet-FOIL datasets to systematically evaluate the existing metrics. VATEX-EVAL experiments demonstrate that EMScore has higher human correlation and lower reference dependency. ActivityNet-FOIL experiment verifies that EMScore can effectively identify "hallucinating" captions. Code and datasets are available at https://***/shiyaya/emscore.

关键词： Measurement Visualization computer vision Correlation Systematics Computational modeling Semantics

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Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis

Systematic Architectural Design of Scale Transformed Attenti...

引用

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

作者： Andrew Hryniowski Alexander Wong Vision and Image Processing Research Group University of Waterloo Waterloo Artificial Intelligence Institute Waterloo DarwinAI Corp. Waterloo

Self-attention mechanisms are commonly included in a convolutional neural networks to achieve an improved efficiency performance balance. However, adding self-attention mechanisms adds additional hyperparameters to tune for the application at hand. In this work we propose a novel type of DNN analysis called Multi-Scale Class Representational Response Similarity Analysis (ClassRep-Sim) which can be used to identify specific design interventions that lead to more efficient self-attention convolutional neural network architectures. Using insights grained from ClassRepSim we propose the Spatial Transformed Attention Condenser (STAC) module, a novel attention-condenser based self-attention module. We show that adding STAC modules to ResNet style architectures can result in up to a 1.6% increase in top-1 accuracy compared to vanilla ResNet models and up to a 0.5% increase in top-1 accuracy compared to SENet models on the ImageNet64x64 dataset, at the cost of up to 1.7% increase in FLOPs and 2x the number of parameters. In addition, we demonstrate that results from ClassRepSim analysis can be used to select an effective parameterization of the STAC module resulting in competitive performance compared to an extensive parameter search.

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GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping

GOAL: Generating 4D Whole-Body Motion for Hand-Object Graspi...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Taheri, Omid Choutas, Vasileios Black, Michael J. Tzionas, Dimitrios Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Generating digital humans that move realistically has many applications and is widely studied, but existing methods focus on the major limbs of the body, ignoring the hands and head. Hands have been separately studied, but the focus has been on generating realistic static grasps of objects. To synthesize virtual characters that interact with the world, we need to generate full-body motions and realistic hand grasps simultaneously. Both sub-problems are challenging on their own and, together, the state space of poses is significantly larger, the scales of hand and body motions differ, and the whole-body posture and the hand grasp must agree, satisfy physical constraints, and be plausible. Additionally, the head is involved because the avatar must look at the object to interact with it. For the first time, we address the problem of generating full-body, hand and head motions of an avatar grasping an unknown object. As input, our method, called GOAL, takes a 3D object, its pose, and a starting 3D body pose and shape. GOAL outputs a sequence of whole-body poses using two novel networks. First, GNet generates a goal whole-body grasp with a realistic body, head, arm, and hand pose, as well as handobject contact. Second, MNet generates the motion between the starting and goal pose. This is challenging, as it requires the avatar to walk towards the object with footground contact, orient the head towards it, reach out, and grasp it with a realistic hand pose and hand-object contact. To achieve this the networks exploit a representation that combines SMPL-X body parameters and 3D vertex offsets. We train and evaluate GOAL, both qualitatively and quantitatively, on the GRAB dataset. Results show that GOAL generalizes well to unseen objects, outperforming baselines. A perceptual study shows that GOAL's generated motions approach the realism of GRAB's ground truth. GOAL takes a step towards generating realistic full-body object grasping motion. Our models and code are available

关键词： Solid modeling computer vision Three-dimensional displays Codes Tracking Shape Avatars

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Gaze Scanpath Transformer: Predicting Visual Search Target by Spatiotemporal Semantic Modeling of Gaze Scanpath

Gaze Scanpath Transformer: Predicting Visual Search Target b...

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

作者： Takumi Nishiyasu Yoichi Sato The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a new method, called the Gaze Scanpath Transformer, for predicting a search target category during a visual search task. Previous methods for estimating visual search targets focus solely on the image features at gaze fixation positions. As a result, the previous methods are unable to take into account the spatiotemporal information of gaze scanpaths and lack consideration of the semantic interrelationships between objects on gaze fixations. In contrast, our method can estimate a visual search target based on the spatiotemporal information of a gaze scanpath and interrelationships among image semantic features at gaze fixation positions. This is achieved by embedding the position and the duration of each fixation, and the order of fixations into the image semantic features, and by using the model’s attention mechanism to facilitate information exchange and emphasis on image semantic features at gaze fixation positions. Evaluation using the COCO-Search18 dataset demonstrates that our proposed method achieves significant performance improvements over other state-ofthe-art baseline models for search target prediction.

关键词： Visualization computer vision conferences Computational modeling Semantics Predictive models Transformers

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