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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是11-20 订阅

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Modality-Specific Strategies for Medical Image Segmentation Using Lightweight SAM Architectures

Modality-Specific Strategies for Medical Image Segmentation...

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/cvf conference on computer vision and pattern recognition, cvpr 2024

作者： Dao, Thuy Ye, Xincheng Scarsbrook, Joshua Balarupan, Gowrienanthan Ribeiro, Fernanda L. Bollmann, Steffen School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Queensland Digital Health Centre University of Queensland Brisbane Australia

ISBN: (纸本)9783031818530

Medical image segmentation tasks are often intricate and require medical domain expertise. Recent advancements in deep learning have expedited these demanding tasks, transitioning from specialized models tailored to each task to versatile foundation models capable of accommodating various image modalities. However, many of these foundation models are optimized for GPU computation, necessitating significant computational resources and constraining their practical utility in clinical settings. Furthermore, their variable accuracy across modalities and novel domains undermines their reliability in clinical practice. To address these limitations, we undertake a comparative investigation into deploying medical image segmentation models on CPU, focusing on accuracy and runtime efficiency, as part of the "cvpr 2024: Segment Anything In Medical Images On Laptop" challenge. Our methodology employs different models customized for each modality, including pre-trained EfficientViT-SAM and LiteMedSAM to yield the most precise and efficient outcomes. Additionally, to bolster model performance for datasets featuring small regions of interest, such as PET scans, we integrate a majority voting mechanism. We optimize runtime using the OpenVINO format within a C++ inference script. This approach improves inference runtime while maintaining competitive accuracy, achieving an average DSC score of 0.86 on the validation set and 0.75 on the testing set with an average runtime of 4.61 s on the testing set. Notably, given that most modalities are evaluated in a zero-shot manner, our findings suggest that the zero-shot capability of foundation models can be further refined through dataset-specific inference strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： C++ (programming language)

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Segment Anything in Medical Images with nnUNet

Segment Anything in Medical Images with nnUNet

引用

International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/cvf conference on computer vision and pattern recognition, cvpr 2024

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. This indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Learning Visual-Semantic Hierarchical Attribute Space for Interpretable Open-Set recognition

Learning Visual-Semantic Hierarchical Attribute Space for In...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Xu, Zhuo Xiang, Xiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology National Key Lab of Multi-Spectral Information Intelligent Processing Technology Wuhan China Peng Cheng Lab China

ISBN: (纸本)9798331510831

In the field of open-set recognition, conventional models often focus on addressing challenges within a single hierarchical category, and these methods frequently lack inter-pretability. In this paper, we propose a novel solution that utilizes attributes and hierarchical relationships to achieve interpretable open-set recognition. Our method is centered around the visual-semantic attribute space. By leveraging hierarchy division, we can decompose the attributes into more granular components, thereby yielding additional performance improvements. When confronted with an unfamiliar object, our method not only classifies it as an unknown category but also provides insights into the broader category and its associated attributes. This capability enhances interpretability by offering valuable information regarding the potential category and characteristics of the object. Experimental results demonstrate great performance improvements compared to existing methods. © 2025 ieee.

关键词： Hierarchical systems

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Channel Propagation Networks for Refreshable vision Transformer

Channel Propagation Networks for Refreshable Vision Transfor...

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2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Go, Junhyeong Ryu, Jongbin Ajou University Korea Republic of

ISBN: (纸本)9798331510831

In this paper, we introduce the Channel Propagation method, which aims to increase the channels of the vision Transformer systematically. Skip connections are commonly acknowledged as a propagation approach that improves the stability of the performance in vision Transformers. Nevertheless, it is important to note that these skip connections may give rise to the problem of over-smoothing, wherein similar features are represented in multiple layers. To tackle this matter, our proposed approach for Channel Propagation in vision Transformers retains the present signal information while concurrently propagating location-specific signals in a newly introduced channel dimension. On the other hand, the proposed Channel Propagation method effectively maintains the integrity of identity representation while simultaneously including patch-wise location-specific supervision by introducing a new channel dimension. The inclusion of this approach in vision Transformers mitigates the issue of over-smoothing while also improving the performance of visual recognition tasks. In our experiments, we confirm that the proposed method is effective for various visual recognition tasks. Specifically, our method demonstrates enhanced performance when implemented on vision Transformer models;the classification accuracy is increased considerably for plain and hierarchical architectures on the ImageNet dataset. © 2025 ieee.

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Focusing on what to Decode and what to Train: SOV Decoding with Specific Target Guided DeNoising and vision Language Advisor

Focusing on what to Decode and what to Train: SOV Decoding w...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Chen, Junwen Wang, Yingcheng Yanai, Keiji The University of Electro-Communications Department of Informatics Tokyo Japan

ISBN: (纸本)9798331510831

Recent transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOID) task by leveraging the detection of DETR and the prior knowledge of vision-Language Model (VLM). However, these methods suffer from extended training times and complex optimization due to the entanglement of object detection and HOI recognition during the decoding process. Especially, the query embeddings used to predict both labels and boxes suffer from ambiguous representations, and the gap between the prediction of HOI labels and verb labels is not considered. To address these challenges, we introduce SOV-STG-VLA with three key components: Subject-Object-Verb (SOV) decoding, Specific Target Guided (STG) denoising, and a vision-Language Advisor (VLA). Our SOV decoders disentangle object detection and verb recognition with a novel interaction region representation. The STG denoising strategy learns label embeddings with ground-truth information to guide the training and inference. Our SOV-STG achieves a fast convergence speed and high accuracy and builds a foundation for the VLA to incorporate the prior knowledge of the VLM. We introduce a vision advisor decoder to fuse both the interaction region information and the VLM's vision knowledge and a Verb-HOI prediction bridge to promote interaction representation learning. Our VLA notably improves our SOV-STG and achieves SOTA performance with one-sixth of training epochs compared to recent SOTA. Code and models are available at https://***/cjw2021/SOV-STG-VLA. © 2025 ieee.

关键词： Decoding

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Focusing on what to Decode and what to Train: SOV Decoding with Specific Target Guided DeNoising and vision Language Advisor

Focusing on what to Decode and what to Train: SOV Decoding w...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Junwen Chen Yingcheng Wang Keiji Yanai Department of Informatics The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： Training Bridges Representation learning Visualization Noise reduction Object detection Transformers Decoding Optimization Convergence

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CoMoGAN: continuous model-guided image-to-image translation

CoMoGAN: continuous model-guided image-to-image translation

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

作者： Pizzati, Fabio Cerri, Pietro de Charette, Raoul INRIA Vislab Le Chesnay Rocquencourt France Vislab Parma Pr Italy INRIA Le Chesnay Rocquencourt France

ISBN: (纸本)9781665445092

CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normalization layer and residual mechanism, which together disentangle image content from position on target manifold. We rely on naive physics-inspired models to guide the training while allowing private model/translations features. CoMoGAN can be used with any GAN backbone and allows new types of image translation, such as cyclic image translation like timelapse generation, or detached linear translation. On all datasets, it outperforms the literature.

关键词： Manifolds Training computer vision Codes Computational modeling pattern recognition

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Meta Pseudo Labels

Meta Pseudo Labels

引用

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

作者： Hieu Pham Dai, Zihang Xie, Qizhe Le, Quoc, V Google AI Brain Team Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing state-of-the-art [16]. Like Pseudo Labels, Meta Pseudo Labels has a teacher network to generate pseudo labels on unlabeled data to teach a student network. However, unlike Pseudo Labels where the teacher is fixed, the teacher in Meta Pseudo Labels is constantly adapted by the feedback of the student's performance on the labeled dataset. As a result, the teacher generates better pseudo labels to teach the student.(1)

关键词： computer vision Semisupervised learning Benchmark testing pattern recognition Standards

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A Sliced Wasserstein Loss for Neural Texture Synthesis

A Sliced Wasserstein Loss for Neural Texture Synthesis

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

作者： Heitz, Eric Vanhoey, Kenneth Chambon, Thomas Belcour, Laurent Unity Technol Grenoble France

ISBN: (纸本)9781665445092

We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognition (e.g. VGG-19). The underlying mathematical problem is the measure of the distance between two distributions in feature space. The Gram-matrix loss is the ubiquitous approximation for this problem but it is subject to several shortcomings. Our goal is to promote the Sliced Wasserstein Distance as a replacement for it. It is theoretically proven, practical, simple to implement, and achieves results that are visually superior for texture synthesis by optimization or training generative neural networks.

关键词： Training Neural networks computer architecture Production Tools Feature extraction Complexity theory

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Visual Navigation with Spatial Attention

Visual Navigation with Spatial Attention

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

作者： Mayo, Bar Hazan, Tamir Tal, Ayellet Technion Haifa Israel

ISBN: (纸本)9781665445092

This work focuses on object goal visual navigation, aiming at finding the location of an object from a given class, where in each step the agent is provided with an egocentric RGB image of the scene. We propose to learn the agent's policy using a reinforcement learning algorithm. Our key contribution is a novel attention probability model for visual navigation tasks. This attention encodes semantic information about observed objects, as well as spatial information about their place. This combination of the "what" and the "where" allows the agent to navigate toward the sought-after object effectively. The attention model is shown to improve the agent's policy and to achieve state-of-the-art results on commonly-used datasets.

关键词： Visualization computer vision Navigation Semantics Reinforcement learning pattern recognition Task analysis

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