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

PATHY

誤傳

https://doi.org/10.1007/978-3-031-73235-5artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer

MAPLE

978-3-031-73234-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

ethnology

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242308.jpg

adduction

,Mahalanobis Distance-Based Multi-view Optimal Transport for?Multi-view Crowd Localization,hose long-axis and short-axis directions are guided by the view ray direction. Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized. Finally, we propos

Living-Will

,RAW-Adapter: Adapting Pre-trained Visual Model to?Camera RAW Images,ubsequent high-level networks. Additionally, RAW-Adapter is a general framework that could be used in various computer vision frameworks. Abundant experiments under different lighting conditions have shown our algorithm’s state-of-the-art (SOTA) performance, demonstrating its effectiveness and effic

Generalize

不能平靜

,AFreeCA: Annotation-Free Counting for?All,y classifier-guided method for dividing an image into patches containing objects that can be reliably counted. Consequently, we can generate counting data for any type of object and count them in an unsupervised manner. Our approach outperforms unsupervised and few-shot alternatives and is not restr

連詞

,Adversarially Robust Distillation by?Reducing the?Student-Teacher Variance Gap,nder an increasing perturbation radius) correlates negatively with the gap between the feature variance evaluated on testing adversarial samples and testing clean samples. Such a negative correlation exhibits a strong linear trend, suggesting that aligning the feature covariance of the student model

Alienated

,Hierarchical Temporal Context Learning for?Camera-Based Semantic Scene Completion,ne-grained contextual correspondence modeling. Subsequently, to dynamically compensate for incomplete observations, we adaptively refine the feature sampling locations based on initially identified locations with high affinity and their neighboring relevant regions. Our method ranks . on the Semanti