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

Interpolate

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

arrhythmic

,Exact Diffusion Inversion via?Bidirectional Integration Approximation,text inversion [.]. However, the above methods introduce considerable computational overhead. In this paper, we propose a new technique, . (BDIA), to perform exact diffusion inversion with negligible computational overhead. We consider a family of second order integration algorithms obtained by aver

討好美人

,Textual Query-Driven Mask Transformer for?Domain Generalized Segmentation,ext embeddings of vision-language models. We employ the text embeddings as object queries within a transformer-based segmentation framework (textual object queries). These queries are regarded as a domain-invariant basis for pixel grouping in DGSS. To leverage the power of textual object queries, we

完成才能戰(zhàn)勝

Occlusion

施加

,Object-Centric Diffusion for?Efficient Video Editing,o inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and sug

施加

MOT

,McGrids: Monte Carlo-Driven Adaptive Grids for?Iso-Surface Extraction,etric shapes with complicated geometric details, many existing algorithms suffer from high computational costs and memory usage. This paper proposes McGrids, a novel approach to improve the efficiency of iso-surface extraction. The key idea is to construct adaptive grids for iso-surface extraction r

Amenable

相信

,Adapt2Reward: Adapting Video-Language Models to?Generalizable Robotic Rewards via?Failure Prompts,einforcement learning and planning for such robotic agents is a generalizable reward function. Recent advances in vision-language models, such as CLIP, have?shown remarkable performance in the domain of deep learning, paving?the way for open-domain visual recognition. However, collecting data?on rob