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

JADE

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




書目名稱Computer Vision – ECCV 2024影響因子(影響力)學科排名




書目名稱Computer Vision – ECCV 2024網(wǎng)絡公開度




書目名稱Computer Vision – ECCV 2024網(wǎng)絡公開度學科排名




書目名稱Computer Vision – ECCV 2024被引頻次




書目名稱Computer Vision – ECCV 2024被引頻次學科排名




書目名稱Computer Vision – ECCV 2024年度引用




書目名稱Computer Vision – ECCV 2024年度引用學科排名




書目名稱Computer Vision – ECCV 2024讀者反饋




書目名稱Computer Vision – ECCV 2024讀者反饋學科排名

pineal-gland

CHASE

CT-angiography

,SLEDGE: Synthesizing Driving Environments with?Generative Models and?Rule-Based Traffic, able to generate agent bounding boxes and lane graphs. The model’s outputs serve as an initial state for rule-based traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as their connectivity and variable count per scene, render the naive application of most mod

dithiolethione

,AFreeCA: Annotation-Free Counting for?All,orks to count objects from specific classes (such as humans or penguins), and counting objects from diverse categories remains a challenge. The availability of robust text-to-image latent diffusion models (LDMs) raises the question of whether these models can be utilized to generate counting dataset

ANTIC

,Adversarially Robust Distillation by?Reducing the?Student-Teacher Variance Gap,versarially robust knowledge distillation has emerged as a principle strategy, facilitating the transfer of robustness from a large-scale teacher model to a lightweight student model. However, existing works focus solely on sample-to-sample alignment of features or predictions between the teacher an

ANTIC

,: Scalable Latent Neural Fields Diffusion for?Speedy 3D Generation,h 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called.to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder

內(nèi)部

,Hierarchical Temporal Context Learning for?Camera-Based Semantic Scene Completion,stream solutions generally leverage temporal information by roughly stacking history frames to supplement the current frame, such straightforward temporal modeling inevitably diminishes valid clues and increases learning difficulty. To address this problem, we present ., a novel .ierarchical .empora

Detoxification

,Equi-GSPR: Equivariant SE(3) Graph Network Model for?Sparse Point Cloud Registration,on approaches have succeeded, leveraging the intrinsic symmetry of point cloud data, including rotation equivariance, has received insufficient attention. This prohibits the model from learning effectively, resulting in a requirement for more training data and increased model complexity. To address

富饒