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Active Transferability Estimation

Active Transferability Estimation

引用

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

作者： Tarun Ram Menta Surgan Jandial Akash Patil Saketh Bachu Vimal K. B Balaji Krishnamurthy Vineeth N. Balasubramanian Mausoom Sarkar Chirag Agarwal Adobe Systems Indian Institute of Technology Madras Indian Institute of Technology Hyderabad Harvard University Agyeya Foundation

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensive fine-tuning. Inspired by active learning techniques, we propose ACT (ACtive Transferability), a new strategy to improve the performance of transferability estimation methods, by leveraging an informative subset of the target data. By leveraging the model’s internal and output representations, we introduce two techniques – class-agnostic and class-aware – to identify informative subsets and show that ACT can be applied to any existing transferability metric to improve their performance and reliability. Our experimental results across multiple source model architectures, target datasets, and transfer learning tasks show that ACT metrics are consistently better or on par with the state-of-the-art transferability metrics.

关键词： Measurement computer vision Computational modeling conferences Transfer learning Estimation computer architecture

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ExerAIde: AI-assisted Multimodal Diagnosis for Enhanced Sports Performance and Personalised Rehabilitation

ExerAIde: AI-assisted Multimodal Diagnosis for Enhanced Spor...

引用

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

作者： Ahmed Qazi Asim Iqbal Tibbling Technologies

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The quest for personalized sports therapy has long been a concern for practitioners and patients alike, aiming for recovery protocols that transcend the one-size-fits-all approach. In this study, we introduce a novel framework for personalized sports therapy through automated joint movement analysis. By synthesizing the analytical capabilities of a Random Forest Classifier (RFC) with a Vector Quantized Variational AutoEncoder (VQ-VAE), we systematically discern the nuanced kinematic differences between healthy and pathological exercise movements. The RFC prioritizes the joints by their discriminative influence on movement healthiness, which informs the VQ-VAE’s derivation of a distilled list of pivotal joints. This dual-model approach not only identifies a hierarchy of joint importance but also ascertains the minimal subset of joints critical for distinguishing between healthy and unhealthy movement patterns. The resultant data-driven insight into joint-specific dynamics underpins the development of targeted, individualized rehabilitation programs. Our results exhibit promising directions in sports therapy, showcasing the potential of machine learning in developing personalized therapeutic interventions.

关键词： Pathology computer vision Protocols conferences Medical treatment Kinematics Vectors

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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...

引用

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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LOFI: LOng-tailed FIne-Grained Network for Food recognition

LOFI: LOng-tailed FIne-Grained Network for Food Recognition

引用

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

作者： Jesús M. Rodríguez-de-Vera Imanol G. Estepa Marc Bolaños Bhalaji Nagarajan Petia Radeva Universitat de Barcelona Barcelona Spain AIGecko Technologies SL Barcelona Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Food recognition plays a crucial role in several healthcare applications. Nevertheless, it presents significant computer vision challenges such as long-tailed and fine-grained distributions that hinder its progress. In this work, we propose LOFI, a Long-tailed Fine-grained Network aimed specifically at tackling these food recognition challenges by improving the feature learning capabilities of food recognition models. Specifically, we improve vanilla R-CNN architecture by tailoring it for food recognition. We design an efficient multi-task framework for fine-grained food recognition, which exploits the lexical similarity of dishes during training to improve the discriminative ability of the network. Secondly, we include a Graph Confidence Propagation module based on graph neural networks to aggregate the information of overlapping detections and refine the final prediction of the network. Extensive analysis and ablations of different components of LOFI highlight that it successfully addresses the targeted problems and leads to noticeable gains in performance. Remarkably, the proposed method achieves competitive results and outperforms the current state-of-the-art methods in three public food benchmarks: UECFood-256, AiCrowd Food Challenge 2022, and UECFood-100 segmented.

关键词： Training Representation learning computer vision conferences Computational modeling Medical services computer architecture

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Test Time Training for Industrial Anomaly Segmentation

Test Time Training for Industrial Anomaly Segmentation

引用

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

作者： Alex Costanzino Pierluigi Zama Ramirez Mirko Del Moro Agostino Aiezzo Giuseppe Lisanti Samuele Salti Luigi Di Stefano Department of Computer Science and Engineering (DISI) CVLAB University of Bologna Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality control. While existing methods excel in generating anomaly scores for each pixel, practical applications require producing a binary segmentation to identify anomalies. Due to the absence of labeled anomalies in many real scenarios, standard practices binarize these maps based on some statistics derived from a validation set containing only nominal samples, resulting in poor segmentation performance. This paper addresses this problem by proposing a test time training strategy to improve the segmentation performance. Indeed, at test time, we can extract rich features directly from anomalous samples to train a classifier that can discriminate defects effectively. Our general approach can work downstream to any AD&S method that provides an anomaly score map as output, even in mul-timodal settings. We demonstrate the effectiveness of our approach over baselines through extensive experimentation and evaluation on MVTec AD and MVTec 3D-AD.

关键词： Training computer vision conferences Quality control Feature extraction pattern recognition Standards

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Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey

Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Sur...

引用

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

作者： Nicolas Chahine Marcos V. Conde Daniela Carfora Gabriel Pacianotto Benoit Pochon Sira Ferradans Radu Timofte Zhichao Duan Xinrui Xu Yipo Huang Quan Yuan Xiangfei Sheng Zhichao Yang Leida Li Haotian Fan Fangyuan Kong Yifang Xu Wei Sun Weixia Zhang Yanwei Jiang Haoning Wu Zicheng Zhang Jun Jia Yingjie Zhou Zhongpeng Ji Xiongkuo Min Weisi Lin Guangtao Zhai Xiaoqi Wang Junqi Liu Zixi Guo Yun Zhang Zewen Chen Wen Wang Juan Wang Bing Li DXOMARK CAIDAS & IFI Computer Vision Lab University of Würzburg Xidian University ByteDance Inc Shanghai Jiao Tong University Nanyang Technological University Huawei School of Electronics and Communication Engineering Sun Yat-sen University China State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Jiaotong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results. This challenge aims to obtain an efficient deep neural network capable of estimating the perceptual quality of real portrait photos. The methods must generalize to diverse scenes and diverse lighting conditions (indoor, outdoor, low-light), movement, blur, and other challenging conditions. In the challenge, 140 participants registered, and 35 submitted results during the challenge period. The performance of the top 5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in Portrait Quality Assessment.

关键词： Surveys computer vision Reviews conferences Lighting Artificial neural networks Quality assessment pattern recognition

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GenVideo: One-shot target-image and shape aware video editing using T2I diffusion models

GenVideo: One-shot target-image and shape aware video editin...

引用

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

作者： Sai Sree Harsha Ambareesh Revanur Dhwanit Agarwal Shradha Agrawal Adobe Inc.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video editing methods based on diffusion models that rely solely on a text prompt for the edit are hindered by the limited expressive power of text prompts. Thus, incorporating a reference target image as a visual guide becomes desirable for precise control over edit. Also, most existing methods struggle to accurately edit a video when the shape and size of the object in the target image differ from the source object. To address these challenges, we propose "GenVideo" for editing videos leveraging target-image aware T2I models. Our approach handles edits with target objects of varying shapes and sizes while maintaining the temporal consistency of the edit using our novel target and shape aware InvEdit masks. Further, we propose a novel target-image aware latent noise correction strategy during inference to improve the temporal consistency of the edits. Experimental analyses indicate that GenVideo can effectively handle edits with objects of varying shapes, where existing approaches fail.

关键词： Visualization computer vision Shape conferences Pipelines Noise Wheels

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Bush Detection for vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods

Bush Detection for Vision-based UGV Guidance in Blueberry Or...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Filipović, Vladan Stefanović, Dimitrije Pajević, Nina Grbović, Željana Djuric, Nemanja Panić, Marko BioSense Institute Novi Sad Serbia

ISBN: (纸本)9798350302493

Object detection has reached strong performance in the last decade, having seen its usage spreading to various application areas, such as medicine, transportation, sports, and others. However, one of the more underutilized areas where advanced detection methods have yet to fully fulfill their promise is in the area of agriculture, where a strong potential exists for applying learned models to achieve practical, real-world impact affecting a large number of people. In this work, we focus on this application area and consider the problem of orchard guidance for ground robots, focusing on obstacle and plant detection from RGB camera images. First, we present an overview of public data sets used to train models to detect relevant objects from camera images and other sensor inputs. Then we introduce a novel data set collected in blueberry orchards that contains camera images in various conditions and provides blueberry bushes as targets for detection. The introduced data set provides the research community with a novel task of blueberry bush detection, which was not commonly considered thus far due to the lack of relevant data sets. We describe a detailed analysis of the data set, and finally provide an experimental study with several state-of-the-art deep object detection models, that set a baseline for the performance on this novel data set. The data set is made available online, enriching the variability of the existing tasks in the field and supporting further development of smart agriculture applications. © 2023 ieee.

关键词： Object detection

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Video Representation Learning for Conversational Facial Expression recognition Guided by Multiple View Reconstruction

Video Representation Learning for Conversational Facial Expr...

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

作者： Valeriya Strizhkova Laura M. Ferrari Hadi Kachmar Antitza Dantcheva François Brémond INRIA Université Côte D’Azur

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Conversational facial expression recognition entails challenges such as handling of facial dynamics, small available datasets, low-intensity and fine-grained emotional expressions and extreme face angle. Towards addressing these challenges, we propose the Masking Action Units and Reconstructing multiple Angles (MAURA) pre-training. MAURA is an efficient self-supervised method that permits the use of small datasets, while preserving end-to-end conversational facial expression recognition with vision Transformer. MAURA masks videos using the location with active Action Units and reconstructs synchronized multi-view videos, thus learning the dependencies between muscle movements and encoding information, which might only be visible in few frames and/or in certain views. Based on one view (e.g., frontal), the encoder reconstructs other views (e.g., top, down, laterals). Such masking and reconstructing strategy provides a powerful representation, beneficial in facial expression downstream tasks. Our experimental analysis shows that we consistently outperform the state-of-the-art in the challenging settings of low-intensity and fine-grained conversational facial expression recognition on four datasets including in-the-wild DFEW, CMU-MOSEI, MFA and multi-view MEAD. Our results suggest that MAURA is able to learn robust and generic video representations.

关键词： Representation learning computer vision Emotion recognition Face recognition conferences Muscles Transformers

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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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