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

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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

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,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

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,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

带伤害

,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

带伤害

,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

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