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Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

Rip Current Segmentation: A Novel Benchmark and YOLOv8 Basel...

引用

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

作者： Andrei Dumitriu Florin Tatui Florin Miron Radu Tudor Ionescu Radu Timofte Computer Vision Lab CAIDAS & IFI University of Würzburg Germany University of Bucharest Romania

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing 2,466 images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising 17 drone videos (comprising about 24K frames) captured at 30 FPS, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88.94% on the validation dataset and 81.21% macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://***/Irikos/rip_currents.

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The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

The 6th Affective Behavior Analysis in-the-wild (ABAW) Compe...

引用

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

作者： Dimitrios Kollias Panagiotis Tzirakis Alan Cowen Stefanos Zafeiriou Irene Kotsia Alice Baird Chris Gagne Chunchang Shao Guanyu Hu Queen Mary University of London UK Hume AI USA Imperial College London UK Cogitat UK Xi’an Jiaotong University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with ieee CVPR 2024. The 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Competition focuses on affect related benchmarking tasks and comprises of five sub-challenges: i) Valence-Arousal Estimation (the target is to estimate two continuous affect dimensions, valence and arousal), ii) Expression recognition (the target is to recognise between the mutually exclusive classes of the 7 basic expressions and ’other’), iii) Action Unit Detection (the target is to detect 12 action units), iv) Compound Expression recognition (the target is to recognise between the 7 mutually exclusive compound expression classes), and v) Emotional Mimicry Intensity Estimation (the target is to estimate six continuous emotion dimensions). In the paper, we present these Challenges, describe their respective datasets and challenge protocols (we outline the evaluation metrics) and present the baseline systems and top performing teams’ per Challenge, as well as their obtained performance. More information for the Competition can be found in: https://***/6th.

关键词： Measurement Emotion recognition computer vision Protocols Target recognition Databases conferences

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GRASP-GCN: Graph-Shape Prioritization for Neural Architecture Search under Distribution Shifts

GRASP-GCN: Graph-Shape Prioritization for Neural Architectur...

引用

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

作者： Sofia Casarin Oswald Lanz Sergio Escalera Free University of Bozen-Bolzano Bolzano Italy Computer Vision Center Barcelona Spain Universitat de Barcelona Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Architecture Search (NAS) methods have shown to output networks that largely outperform human-designed networks. However, conventional NAS methods have mostly tackled the single dataset scenario, incuring in a large computational cost as the procedure has to be run from scratch for every new dataset. In this work, we focus on predictor-based algorithms and propose a simple and efficient way of improving their prediction performance when dealing with data distribution shifts. We exploit the Kronecker-product on the randomly wired search-space and create a small NAS benchmark composed of networks trained over four different datasets. To improve the generalization abilities, we propose GRASP-GCN, a ranking Graph Convolutional Network that takes as additional input the shape of the layers of the neural networks. GRASP-GCN is trained with the not-at-convergence accuracies, and improves the state-of-the-art of 3.3 % for Cifar-10 and increasing moreover the generalization abilities under data distribution shift.

关键词： Training Accuracy Shape Graph convolutional networks Neural networks computer architecture Object detection

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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...

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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

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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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Multi-View Action recognition for Distracted Driver Behavior Localization

Multi-View Action Recognition for Distracted Driver Behavior...

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

作者： Yuehuan Xu Shuai Jiang Zhe Cui Fei Su Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The detection and recognition of distracted driving behaviors has emerged as a new vision task with the rapid development of computer vision, which is considered as a challenging temporal action localization (TAL) problem in computer vision. The primary goal of temporal localization is to determine the start and end time of actions in untrimmed videos. Currently, most state-of-the-art temporal localization methods adopt complex architectures, which are cumbersome and time-consuming. In this paper, we propose a robust and efficient two-stage framework for distracted behavior classification-localization based on the sliding window approach, which is suitable for untrimmed naturalistic driving videos. To address the issues of high similarity among different behaviors and interference from background classes, we propose a multi-view fusion and adaptive thresholding algorithm, which effectively reduces missing detections. To address the problem of fuzzy behavior boundary localization, we design a post-processing procedure that achieves fine localization from coarse localization through post connection and candidate behavior merging criteria. In the AICITY2024 Task3 TestA, our method performs well, achieving Average Intersection over Union(AIOU) of 0.6080 and ranking eighth in AICITY2024 Task3. Our code will be released in the near future.

关键词： Location awareness computer vision Filtering Splicing Urban areas Merging Interference

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CSANet: High Speed Channel Spatial Attention Network for Mobile ISP

CSANet: High Speed Channel Spatial Attention Network for Mob...

引用

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

作者： Hsyu, Ming-Chun Liu, Chih-Wei Chen, Chao-Hung Chen, Chao-Wei Tsai, Wen-Chia Ind Technol Res Inst Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781665448994

The Image Signal Processor (ISP) is a customized device to restore RGB images from the pixel signals of CMOS image sensor. In order to realize this function, a series of processing units are leveraged to tackle different artifacts, such as color shifts, signal noise, moire effects, and so on, that are introduced from the photo-capturing devices. However, tuning each processing unit is highly complicated and requires a lot of experience and effort from image experts. In this paper, a novel network architecture, CSANet, with emphases on inference speed and high PSNR is proposed for end-to-end learned ISP task. The proposed CSANet applies a double attention module employing both channel and spatial attentions. Particularly, its spatial attention is simplified to a light-weighted dilated depth-wise convolution and still performs as well as others. As proof of performance, CSANet won 2nd place in the Mobile AI 2021 Learned Smartphone ISP Challenge with 1st place PSNR score.

关键词： Performance evaluation Runtime Pipelines Network architecture Service-oriented architecture pattern recognition Image restoration

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OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

OpenFed: A Comprehensive and Versatile Open-Source Federated...

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

作者： Dengsheng Chen Vince Junkai Tan Zhilin Lu Enhua Wu Jie Hu Meituan Bytedance Inc. Tsinghua University University of Macau State Key Lab of Computer Science ISCAS University of Chinese Academy of Sciences

Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plugged and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning. The source code of OpenFed is publicly available online at https://***/FederalLab/OpenFed.

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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...

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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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The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks

The Expanding Scope of the Stability Gap: Unveiling its Pres...

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

作者： Sandesh Kamath Albin Soutif-Cormerais Joost Van de Weijer Bogdan Raducanu Department of Computer Science Universitat Autònoma de Barcelona Computer Vision Center Barcelona

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research identified a temporary performance drop on previously learned tasks when transitioning to a new one. This drop is called the stability gap and has great consequences for continual learning: it complicates the direct employment of continually learning since the worse-case performance at task-boundaries is dramatic, it limits its potential as an energy-efficient training paradigm, and finally, the stability drop could result in a reduced final performance of the algorithm. In this paper, we show that the stability gap also occurs when applying joint incremental training of homogeneous tasks. In this scenario, the learner continues training on the same data distribution and has access to all data from previous tasks. In addition, we show that in this scenario, there exists a low-loss linear path to the next minima, but that SGD optimization does not choose this path. We perform further analysis including a finer batch-wise analysis which could provide insights towards potential solution directions.

关键词： Training Continuing education Incremental learning Employment Stability analysis Energy efficiency Trajectory

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