Accelerated co-design of robots through morphological pretraining
专题命中 预训练与数据 :pretraining(title,abstract)
AI 大模型
大语言模型、预训练、指令微调、后训练和语言模型应用。
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :language model(title,abstract)
专题命中 预训练与数据 :foundation model(title,abstract)
Comments Accepted at Image and Vision Computing Journal 2025
专题命中 预训练与数据 :language model(title,abstract)
Comments Accepted by AAAI2025
专题命中 预训练与数据 :foundation model(title);pretraining(abstract)
专题命中 预训练与数据 :foundation model(title,abstract)
Comments Accepted at Winter Conference on Applications of Computer Vision (WACV) 2025. Code and available at https://github.com/AIT-Assistive-Autonomous-Systems/Hopomop
专题命中 预训练与数据 :foundation model(title,abstract)
Comments Accepted for publication at the Electronic Imaging - Autonomous Vehicles and Machines Connference 2025
专题命中 预训练与数据 :foundation model(title,abstract)
Comments The dataset would be available here: https://www.idiap.ch/paper/digi2real Accepted for Publication in WACV 2025
专题命中 预训练与数据 :foundation model(title,abstract)
Comments Accepted to ECCV 2024. Project page: https://andreacaraffa.github.io/freeze
专题命中 预训练与数据 :foundation model(title,abstract)
Comments Accepted at WACV 2025 workshops
专题命中 预训练与数据 :foundation model(title,abstract)
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :pretraining(title,abstract)
Comments 15pages, Accepted by AAAI2025, full paper
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI、cs.LG
Comments 26 pages, 8 figures, best paper award at ML4H 2024
专题命中 预训练与数据 :foundation model(title,abstract)
Comments Project website: https://www.foundir.net
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :language model(title,abstract)
专题命中 预训练与数据 :foundation model(title,abstract)
Comments For MICCAI CMRxRecon Challenge 2024 team CardiAxs
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :foundation model(title,abstract)
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :pretraining(title,abstract)
Comments This work has been submitted to the IEEE for possible publication. Compared to the previous version, large-scale changes were made to make the paper easier to understand for people less familiar with contrastive learning and to make it easier to follow certain arguments. 10 pages, 9 figures
专题命中 预训练与数据 :prompting(title,abstract)
Comments NeurIPS 2024, add some results
专题命中 预训练与数据 :pretraining(title,abstract)
专题命中 预训练与数据 :foundation model(title,abstract)
Comments Project page: https://jiupinjia.github.io/metaearth/
专题命中 预训练与数据 :language model(title,abstract)