找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

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

[复制链接]
楼主: 我没有辱骂
发表于 2025-3-25 03:25:58 | 显示全部楼层
发表于 2025-3-25 09:34:55 | 显示全部楼层
发表于 2025-3-25 13:28:55 | 显示全部楼层
Laura Kelly,Victoria Foster,Anne Hayesodel per task and use the REINFORCE [.] algorithm to patch into a subset of them with a new query image. The resulting Task Vectors guide the model towards performing the task better than the original model. (For code and models see .).
发表于 2025-3-25 19:11:19 | 显示全部楼层
Finding Visual Task Vectors,odel per task and use the REINFORCE [.] algorithm to patch into a subset of them with a new query image. The resulting Task Vectors guide the model towards performing the task better than the original model. (For code and models see .).
发表于 2025-3-26 00:04:05 | 显示全部楼层
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-72774-0978-3-031-72775-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
发表于 2025-3-26 01:21:57 | 显示全部楼层
发表于 2025-3-26 06:23:43 | 显示全部楼层
The Attractiveness of Alternative Medicineouds to facilitate knowledge transfer and propose an innovative hybrid feature augmentation methodology, which enhances the alignment between the 3D feature space and SAM’s feature space, operating at both the scene and instance levels. Our method is evaluated on many widely-recognized datasets and achieves state-of-the-art performance.
发表于 2025-3-26 09:38:38 | 显示全部楼层
发表于 2025-3-26 14:21:46 | 显示全部楼层
Rethinking Peace and Conflict Studiest features..For training our framework, we curate a synthetic event camera dataset featuring diverse scene and motion patterns. Transfer learning performance on downstream dense prediction tasks illustrates the superiority of our method over state-of-the-art approaches.
发表于 2025-3-26 17:03:05 | 显示全部楼层
,LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models,upport GVC and various types of visual prompts by connecting segmentation models with language models. Experimental results demonstrate that our model outperforms other LMMs on Grounding-Bench. Furthermore, our model achieves competitive performance on classic grounding benchmarks like RefCOCO/+/g and Flickr30K Entities.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-6-30 01:24
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表