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

JADE

书目名称Computer Vision – ECCV 2024影响因子(影响力)




书目名称Computer Vision – ECCV 2024影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2024网络公开度




书目名称Computer Vision – ECCV 2024网络公开度学科排名




书目名称Computer Vision – ECCV 2024被引频次




书目名称Computer Vision – ECCV 2024被引频次学科排名




书目名称Computer Vision – ECCV 2024年度引用




书目名称Computer Vision – ECCV 2024年度引用学科排名




书目名称Computer Vision – ECCV 2024读者反馈




书目名称Computer Vision – ECCV 2024读者反馈学科排名

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

内部

,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

富饶