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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 31021 条记录，以下是4221-4230 订阅

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CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers

CuVLER: Enhanced Unsupervised Object Discoveries through Exh...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shahaf Arica Or Rubin Sapir Gershov Shlomi Laufer Technion - Israel Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we introduce Vote Cut, an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. VoteCut employs normalized-cut based graph partitioning, clustering and a pixel voting approach. Additionally, We present CuVLER (Cut-Vote-and-LEaRn), a zero-shot model, trained using pseudo-labels, generated by Vote Cut, and a novel soft target loss to refine segmentation accuracy. Through rigorous evaluations across multiple datasets and several unsupervised setups, our methods demonstrate significant improvements in comparison to previous state-of-the-art models. Our ablation studies further highlight the contributions of each component, revealing the robustness and efficacy of our approach. Collectively, VoteCut and CuVLER pave the way for future advancements in image segmentation. The project code is available on GitHub at https://***/shahaf-arica/CuVLER

关键词： Image segmentation computer vision Codes Accuracy Transformers Robustness pattern recognition

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Scene Essence

Scene Essence

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qiu, Jiayan Yang, Yiding Wang, Xinchao Tao, Dacheng Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ 07030 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an exotic learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the scene. To this end, we devise a novel approach that learns to partition the scene objects into two groups, essential ones and minor ones, under the supervision that if only the essential ones are kept while the minor ones are erased in the input image, a scene recognizer would preserve its original prediction. Specifically, we introduce a learnable graph neural network (GNN) for labelling scene objects, based on which the minor ones are wiped off by an off-the-shelf image inpainter. The features of the inpainted image derived in this way, together with those learned from the GNN with the minor-object nodes pruned, are expected to fool the scene discriminator. Both subjective and objective evaluations on Places365, SUN397, and MIT67 datasets demonstrate that, the learned Scene Essence yields a visually plausible image that convincingly retains the original scene category.

关键词： computer vision Image recognition Graph neural networks pattern recognition Labeling Lenses

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Generalizable Multi-Camera 3D Pedestrian Detection

Generalizable Multi-Camera 3D Pedestrian Detection

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lima, Joao Paulo Roberto, Rafael Figueiredo, Lucas Simoes, Francisco Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil

ISBN: (纸本)9781665448994

We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person's bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re-identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.

关键词： Training computer vision Three-dimensional displays Sensitivity Fuses conferences Pose estimation

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Model Adaptation for Time Constrained Embodied Control

Model Adaptation for Time Constrained Embodied Control

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jaehyun Song Minjong Yoo Honguk Woo Department of Computer Science and Engineering Sungkyunkwan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

When adopting a deep learning model for embodied agents, it is required that the model structure be optimized for specific tasks and operational conditions. Such optimization can be static such as model compression or dynamic such as adaptive inference. Yet, these techniques have not been fully investigated for embodied control systems subject to time constraints, which necessitate sequential decision-making for multiple tasks, each with distinct inference latency limitations. In this paper, we present MoDeC, a time constraint-aware embodied control framework using the modular model adaptation. We formulate model adaptation to varying operational conditions on resource and time restrictions as dynamic routing on a modular network, incorporating these conditions as part of multi-task objectives. Our evaluation across several vision-based embodied environments demonstrates the robustness of MoDeC, showing that it outperforms other model adaptation methods in both performance and adherence to time constraints in robotic manipulation and autonomous driving applications.

关键词： Performance evaluation Adaptation models Multitasking Routing Robustness Safety pattern recognition

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A Universal Encoder Rate Distortion Optimization Framework for Learned Compression

A Universal Encoder Rate Distortion Optimization Framework f...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Jing Li, Bin Li, Jiahao Xiong, Ruiqin Lu, Yan Peking Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665448994

Learning-based image compression has drawn increasing attention in recent years. Despite impressive progress has been made, it still lacks a universal encoder optimization method to seek efficient representation for different images. In this paper, we develop a universal rate distortion optimization framework for learning-based compression, which adaptively optimizes latents and side information together for each image. The proposed framework is independent of network architecture and can be flexibly applied to existing and potential future compression networks. Experimental results demonstrate that we can achieve 6.6% bit rate saving against the latest traditional codec, i.e., VVC, yielding the state-of-the-art compression ratio. Moreover, with the proposed optimization framework, we win the first place in CLIC validation phase for all the three different bit rates in terms of PSNR.

关键词： computer vision Image coding Codecs conferences Bit rate Rate-distortion Optimization methods

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Detecting the Foreground Dynamic Scenes using Gaussian Mixture Model Analysis Compared with Novel Principal Component Analysis 2

Detecting the Foreground Dynamic Scenes using Gaussian Mixtu...

引用

2nd International conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Baswaraju, Swathi New Horizon College of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350325799

Many computer vision applications rely on object recognition in video streams, and the most common technique for doing so is called foreground subtraction. updated backdrop model with a new frame. However, precise object identification is challenging in static and dynamic foregrounds. This study proposes a novel approach. Recently, Novel Principal Component Analysis, also known as (NPCA), has been used extensively across the industry for dimension reduction in image processing, presentation, and pattern identification to this issue that is based on the Gaussian Mixture Model (GMM) technique. A new updating method has been implemented to achieve a more stable model for the dynamic zones. This study provides a comprehensive overview and evaluation of other subtraction methods. The experimental results demonstrate the superiority of the proposed method over numerous top-tier object detection methods. © 2023 IEEE.

关键词： Principal component analysis

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pattern recognition in Apple Orchards During Dormancy and Bud Development 4th

Pattern Recognition in Apple Orchards During Dormancy and ...

引用

4th International conference on Smart Multimedia, ICSM 2024

作者： Sehgal, Bhavya Gandhi, Ritik Cheng, Irene Department of Computing Science University of Alberta EdmontonABT6G 2R3 Canada

ISBN: (纸本)9783031824746

Effective management of apple orchards during dormancy and bud development stages is crucial for optimizing fruit production and tree health. Automation using computer vision and deep learning techniques offers a promising solution for tree segmentation and labeling in complex outdoor environments. However, existing methods face challenges in accurately segmenting trees with bare branches and adapting to changing perspectives. This paper presents innovative solutions for bare tree segmentation and labeling, combining depth sensing and advanced image processing with deep learning approaches. The proposed methods address the limitations of current techniques, offering improved accuracy and robustness. We also introduce a novel tree labeling algorithm that utilizes spatial information and YOLOv4 for accurate tree identification and labeling across video frames. Experimental results demonstrate the effectiveness of our approach in handling complex outdoor conditions and bare tree segmentation. Our research contributes to advancing automated orchard management and precise monitoring during dormancy and bud development stages, enhancing fruit quality and overall orchard productivity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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A Strong Baseline for Vehicle Re-Identification

A Strong Baseline for Vehicle Re-Identification

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Huynh, Su, V Nguyen, Nam H. Nguyen, Ngoc T. Nguyen, Vinh T. Q. Huynh, Chau Chuong Nguyen Cybercore AI Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://***/cybercore-co-ltd/track2_aicity_2021.

关键词： Training computer vision Target tracking Head conferences Urban areas Stacking

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Generating Illustrated Instructions

Generating Illustrated Instructions

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sachit Menon Ishan Misra Rohit Girdhar GenAI Meta Columbia University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a new task of generating “Illustrated Instructions ”, i.e. visual instructions customized to a user's needs. We identify desiderata unique to this task, and formalize it through a suite of automatic and human evaluation metrics, designed to measure the validity, consistency, and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion, which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases, users even prefer it to human-generated articles. Most notably, it enables various new and exciting applications far beyond what static articles on the web can provide, such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation.

关键词： Measurement Visualization computer vision Large language models Text to image Diffusion models pattern recognition

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Action Detection via an Image Diffusion Process

Action Detection via an Image Diffusion Process

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lin Geng Foo Tianjiao Li Hossein Rahmani Jun Liu Singapore University of Technology and Design Lancaster University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Action detection aims to localize the starting and ending points of action instances in untrimmed videos, and predict the classes of those instances. In this paper, we make the observation that the outputs of the action detection task can be formulated as images. Thus, from a novel perspective, we tackle action detection via a three-image generation process to generate starting point, ending point and action-class predictions as images via our proposed Action Detection Image Diffusion (ADI-Diff) framework. Furthermore, since our images differ from natural images and exhibit special properties, we further explore a Discrete Action-Detection Diffusion Process and a Row-Column Transformer design to better handle their processing. Our ADI-Diff framework achieves state-of-the-art results on two widely-used datasets.

关键词： computer vision Casting Image synthesis Diffusion processes Transformers pattern recognition Videos

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