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

HAVEN

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(xué)科排名




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




書目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開度學(xué)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(xué)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(xué)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(xué)科排名

制定法律

,An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for?Large Vision-L, QwenVL-Chat, and Video-LLaVA. We find that the attention computation over visual tokens is extremely inefficient in the deep layers of popular LVLMs, suggesting a need for a sparser approach compared to textual data handling. To this end, we introduce FastV, a versatile plug-and-play method design

偏見(jiàn)

Injunction

沒(méi)血色

,Bridging Different Language Models and?Generative Vision Models for?Text-to-Image Generation,a language model that interprets user prompts and a vision model that generates corresponding images. As language and vision models continue to progress in their respective domains, there is a great potential in exploring the replacement of components in text-to-image diffusion models with more adva

帶傷害

,Tackling Structural Hallucination in?Image Translation with?Local Diffusion,ages, such as unseen tumors in medical images, causing “image hallucination” and risking misdiagnosis. We hypothesize such hallucinations result from local OOD regions in the conditional images. We verify that partitioning the OOD region and conducting separate image generations alleviates hallucina

帶傷害

,Hierarchical Separable Video Transformer for?Snapshot Compressive Imaging,posedness is rooted in the mixed degradation of spatial masking and temporal aliasing. However, previous Transformers lack an insight into the degradation and thus have limited performance and efficiency. In this work, we tailor an efficient reconstruction architecture without temporal aggregation i

盤旋

deceive

Projection