Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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書(shū)目名稱Computer Vision – ECCV 2024影響因子(影響力)




書(shū)目名稱Computer Vision – ECCV 2024影響因子(影響力)學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024被引頻次




書(shū)目名稱Computer Vision – ECCV 2024被引頻次學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024年度引用




書(shū)目名稱Computer Vision – ECCV 2024年度引用學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024讀者反饋




書(shū)目名稱Computer Vision – ECCV 2024讀者反饋學(xué)科排名

茁壯成長(zhǎng)

的染料

腐蝕

,Learning to?Adapt SAM for?Segmenting Cross-Domain Point Clouds,of point clouds. Especially for LiDAR point clouds, the domain discrepancy becomes obvious across varying capture scenes, fluctuating weather conditions, and the diverse array of LiDAR devices in use. Inspired by the remarkable generalization capabilities exhibited by the vision foundation model, SA

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新義

,ViewFormer: Exploring Spatiotemporal Modeling for?Multi-view 3D Occupancy Perception via?View-Guideround by quantifying the physical space into a grid map. The widely adopted projection-first deformable attention, efficient in transforming image features into 3D representations, encounters challenges in aggregating multi-view features due to sensor deployment constraints. To address this issue, w

新義

COMA

擴(kuò)張

Introduction

,Contrastive Learning with?Counterfactual Explanations for?Radiology Report Generation,e automatic report generation models to learn entangled and spurious representations resulting in misdiagnostic reports. To tackle these, we propose a novel .unter.actual .xplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for under