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

backdrop

遍及

,Distilling Knowledge from Large-Scale Image Models for Object Detection, exhibits sparse query locations, thereby facilitating the distillation process. (2) Considering that large-scale detectors are mainly based on DETRs, we propose a Query Distillation (QD) method specifically tailored for DETRs. The QD performs knowledge distillation by leveraging the spatial positio

破布

,Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection,ted extensive experiments on two benchmarks: the low-resolution PKU-DDD17-Car dataset and the high-resolution DSEC dataset. Experimental results show that our method surpasses the state-of-the-art by an impressive margin of . on the DSEC dataset. Besides, our method exhibits significantly better rob

Offset

强制性

corporate

facetious

SAGE

,Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Im generation-guided progression prediction module are introduced. Compared to conventional image-to-image generative models, identity priors regularize and guide the diffusion to focus more on the clinical nuances of the prognosis based on a contrastive learning strategy. The progression prediction

执

reserve