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Cascade Prompt Learning for Vision-Language Model Adaptation

Autor: Wu, Ge, Zhang, Xin, Li, Zheng, Chen, Zhaowei, Liang, Jiajun, Yang, Jian, Li, Xiang

Prompt learning has surfaced as an effective approach to enhance the performance of Vision-Language Models (VLMs) like CLIP when applied to downstream tasks. However, current learnable prompt tokens are primarily used for the single phase of adapting

Externí odkaz: http://arxiv.org/abs/2409.17805

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哥,你好▷线上看完整版 'Give Me Five / Xiao Wu Ge (2022)在线观看[1080P]高清电影

Autor: Bw65m7nbtbvgetr

▷哥,你好▷ 完整版高清- (2022) 电影 |IMax-HD| 在线观看电影 “哥,你好” 完整的电豆瓣電影- !哥,你好! 『哥,你好』 2022! 線上看1080p~!鴨HD 电影完整版在线搜索哥,你好完整版(Give Me Five / Xiao W

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35951b9fb94915a1d3e717487e7dc9bf

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哥,你好 (Give Me Five / Xiao Wu Ge) 在線觀看【1080~HD】免費完整版

Autor: Xiaowuge

哥,你好電影完整版. 哥,你好在線完整版. 手錶哥,你好 (Give Me Five) 中國電影在線字幕. 哥,你好中文版. 在線觀看哥,你好 [普通話] 電影全高清-1080P. 觀看和下載電影(哥,你好) 完整的電影免�

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2348a7aade79db58bba7a5850509dcf4

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Blockchain-based Privacy-Preserving Public Key Searchable Encryption with Strong Traceability

Autor: Han, Yue, Han, Jinguang, Meng, Weizhi, Lai, Jianchang, Wu, Ge

Public key searchable encryption (PKSE) scheme allows data users to search over encrypted data. To identify illegal users, many traceable PKSE schemes have been proposed. However, existing schemes cannot trace the keywords which illegal users searche

Externí odkaz: http://arxiv.org/abs/2312.16954

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哥,你好完整版-(Give Me Five / Xiao Wu Ge)【2022】HD—在线观看和下载完整电影

Autor: Movie

看電影官方网站流媒体《哥,你好》2022年电影,【免费高清】在线观看-哥,你好/Table for Six—中国—动作,冒险,奇幻电影—免费完整视频「1080p|蓝光」哥,你好香港版(@GIMY)線上看小鴨 - 哥,你好台

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6ac889cd6e67c821a2414c38acf8c6d

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A Survey of Historical Learning: Learning Models with Learning History

Autor: Li, Xiang, Wu, Ge, Yang, Lingfeng, Wang, Wenhai, Song, Renjie, Yang, Jian

New knowledge originates from the old. The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models. In this survey, we comprehensively review and summarize the topic--``Historical

Externí odkaz: http://arxiv.org/abs/2303.12992

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