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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

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Proceedings - 2024 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2024

Proceedings - 2024 IEEE/CVF Conference on Computer Vision an...

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2024 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2024

ISBN: (纸本)9798350365474

The proceedings contain 802 papers. The topics discussed include: X-VARS: introducing explainability in football refereeing with multi-modal large language models;a hybrid ANN-SNN architecture for low-power and low-latency visual perception;pseudo-label based unsupervised fine-tuning of a monocular 3D pose estimation model for sports motions;towards efficient audio-visual learners via empowering pre-trained vision transformers with cross-modal adaptation;a dual-mode approach for vision-based navigation in a lunar landing scenario;class similarity transition: decoupling class similarities and imbalance from generalized few-shot segmentation;ReweightOOD: loss reweighting for distance-based OOD detection;Hinge-Wasserstein: estimating multimodal aleatoric uncertainty in regression tasks;and ConPro: learning severity representation for medical images using contrastive learning and preference optimization.

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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

International Challenge on Segment Anything in Medical Image...

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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

ISBN: (纸本)9783031818530

The proceedings contain 16 papers. The special focus in this conference is on Segment Anything in Medical Images on Laptop. The topics include: Filters, Thresholds, and Geodesic Distances for Scribble-Based Interactive Segmentation of Medical Images;Rep-MedSAM: Towards Real-Time and Universal Medical Image Segmentation;Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets;a Light-Weight Universal Medical Segmentation Network for Laptops Based on Knowledge Distillation;taking a Step Back: Revisiting Classical Approaches for Efficient Interactive Segmentation of Medical Images;ExpertsMedSAM: Faster Medical Image Segment Anything with Mixture-of-Experts;efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment;Lite Class-Prompt Tiny-VIT for Multi-modality Medical Image Segmentation;Segment Anything in Medical Images with nnUNet;SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments;RepViT-MedSAM: Efficient Segment Anything in the Medical Images;U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation;Modality-Specific Strategies for Medical Image Segmentation Using Lightweight SAM Architectures;gray’s Anatomy for Segment Anything Model: Optimizing Grayscale Medical Images for Fast and Lightweight Segmentation.

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Proceedings - 2019 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

Proceedings - 2019 IEEE/CVF Conference on Computer Vision an...

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

ISBN: (纸本)9781728125060

The proceedings contain 355 papers. The topics discussed include: MultiNet++: multi-stream feature aggregation and geometric loss strategy for multi-task learning;privacy-preserving action recognition using coded aperture videos;evading face recognition via partial tampering of faces;privacy-preserving annotation of face images through attribute-preserving face synthesis;towards deep neural network training on encrypted data;fooling automated surveillance cameras: adversarial patches to attack person detection;anonymousnet: natural face de-identification with measurable privacy;regularizer to mitigate gradient masking effect during single-step adversarial training;privacy preserving group membership verification and identification;defending against adversarial attacks using random forest;intersection to overpass: instance segmentation on filamentous structures with an orientation-aware neural network and terminus pairing algorithm;and surface parameterization and registration for statistical multiscale atlasing of organ development.

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Proceedings - 2021 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2021

Proceedings - 2021 IEEE/CVF Conference on Computer Vision an...

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2021 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2021

ISBN: (纸本)9781665448994

The proceedings contain 516 papers. The topics discussed include: OmniLayout: room layout reconstruction from indoor spherical panoramas;boosting adversarial robustness using feature level stochastic smoothing;beyond joint demosaicking and denoising: an image processing pipeline for a pixel-bin image sensor;assessment of deep learning based blood pressure prediction from PPG and rPPG signals;towards domain-specific explainable AI: model interpretation of a skin image classifier using a human approach;DAMSL: domain agnostic meta score-based learning;deep learning based spatial-temporal in-loop filtering for versatile video coding;automated tackle injury risk assessment in contact-based sports - a rugby union example;two-stage network for single image super-resolution;and ***: dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery.

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Proceedings - 2020 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2020

Proceedings - 2020 IEEE/CVF Conference on Computer Vision an...

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2020 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2020

ISBN: (纸本)9781728193601

The proceedings contain 523 papers. The topics discussed include: latent fingerprint image enhancement based on progressive generative adversarial network;zero-shot learning in the presence of hierarchically coarsened labels;multivariate confidence calibration for object detection;context-guided super-class inference for zero-shot detection;learning sparse ternary neural networks with entropy-constrained trained ternarization (EC2T);now that i can see, i can improve: enabling data-driven finetuning of CNNs on the edge;enhancing facial data diversity with style-based face aging;a simplified framework for zero-shot cross-modal sketch data retrieval;unsupervised single image super-resolution network (USISResNet) for real-world data using generative adversarial network;cross-regional oil palm tree detection;and leaf spot attention network for apple leaf disease identification.

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Proceedings - 2022 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2022

Proceedings - 2022 IEEE/CVF Conference on Computer Vision an...

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2022 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2022

ISBN: (纸本)9781665487399

The proceedings contain 561 papers. The topics discussed include: CORE: consistent representation learning for face forgery detection;aria: adversarially robust image attribution for content provenance;the reliability of forensic body-shape identification;detecting real-time deep-fake videos using active illumination;on the exploitation of deepfake model recognition;is synthetic voice detection research going into the right direction?;on improving cross-dataset generalization of deepfake detectors;rethinking adversarial examples in wargames;privacy leakage of adversarial training models in federated learning systems;towards comprehensive testing on the robustness of cooperative multi-agent reinforcement learning;robustness and adaptation to hidden factors of variation;adversarial robustness through the lens of convolutional filters;RODD: a self-supervised approach for robust out-of-distribution detection;an empirical study of data-free quantization’s tuning robustness;exploring robustness connection between artificial and natural adversarial examples;and adversarial machine learning attacks against video anomaly detection systems.

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

Proceedings - 2023 IEEE/CVF Conference on Computer Vision an...

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

ISBN: (纸本)9798350302493

The proceedings contain 698 papers. The topics discussed include: learning unbiased classifiers from biased data with meta-learning;robustness against gradient based attacks through cost effective network fine-tuning;gradient attention balance network: mitigating face recognition racial bias via gradient attention;estimating and maximizing mutual information for knowledge distillation;synthetic sample selection for generalized zero-shot learning;training strategies for vision transformers for object detection;does image anonymization impact computer vision training?;ultra-sonic sensor based object detection for autonomous vehicles;improvements to image reconstruction-based performance prediction for semantic segmentation in highly automated driving;zero-shot classification at different levels of granularity;difficulty estimation with action scores for computer vision tasks;detail-preserving self-supervised monocular depth with self-supervised structural sharpening;isolated sign language recognition based on tree structure skeleton images;deep prototypical-parts ease morphological kidney stone identification and are competitively robust to photometric perturbations;wildlife image generation from scene graphs;towards characterizing the semantic robustness of face recognition;high-level context representation for emotion recognition in images;and mitigating catastrophic interference using unsupervised multi-part attention for RGB-IR face recognition.

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Proceedings - 2024 ieee/cvf conference on computer vision and pattern recognition, CVPR 2024

Proceedings - 2024 IEEE/CVF Conference on Computer Vision an...

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2024 ieee/cvf conference on computer vision and pattern recognition, CVPR 2024

ISBN: (纸本)9798350353006

The proceedings contain 2715 papers. The topics discussed include: revisiting adversarial training at scale;SPIDeRS: structured polarization for invisible depth and reflectance sensing;MA-LMM: memory-augmented large multimodal model for long-term video understanding;geometrically-driven aggregation for zero-shot 3D point cloud understanding;TextCraftor: your text encoder can be image quality controller;ViLa-MIL: dual-scale vision-language multiple instance learning for whole slide image classification;HumanNorm: learning normal diffusion model for high-quality and realistic 3D human generation;AnEmpirical study of scaling law for scene text recognition;improving image restoration through removing degradations in textual representations;and steganographic passport: an owner and user verifiable credential for deep model ip protection without retraining.

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U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmen...

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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： Wang, Xin Liu, Xiaoyu Huang, Peng Huang, Pu Hu, Shu Zhu, Hongtu Albany United States School of Physics and Electronics Shandong Normal University Jinan China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Computer and Information Technology Purdue University West Lafayette United States University of North Carolina at Chapel Hill Chapel Hill United States

ISBN: (纸本)9783031818530

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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

Modality-Specific Strategies for Medical Image Segmentation...

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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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