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pro vyhledávání: '"Li, Handong"'
We propose to build omni-modal intelligence, which is capable of understanding any modality and learning universal representations. In specific, we propose a scalable pretraining paradigm, named Multimodal Context (MiCo), which can scale up the numbe
Externí odkaz:
http://arxiv.org/abs/2406.09412
Autor:
Song, Shijia, Li, Handong
Bank crisis is challenging to define but can be manifested through bank contagion. This study presents a comprehensive framework grounded in nonlinear time series analysis to identify potential early warning signals (EWS) for impending phase transiti
Externí odkaz:
http://arxiv.org/abs/2310.10283
Due to the limited scale and quality of video-text training corpus, most vision-language foundation models employ image-text datasets for pretraining and primarily focus on modeling visually semantic representations while disregarding temporal semant
Externí odkaz:
http://arxiv.org/abs/2306.09085
Vision and text have been fully explored in contemporary video-text foundational models, while other modalities such as audio and subtitles in videos have not received sufficient attention. In this paper, we resort to establish connections between mu
Externí odkaz:
http://arxiv.org/abs/2305.18500
Large pre-trained multimodal models have demonstrated significant success in a range of downstream tasks, including image captioning, image-text retrieval, visual question answering (VQA), etc. However, many of these methods rely on image-text pairs
Externí odkaz:
http://arxiv.org/abs/2305.11769
Autor:
Li, Handong1 (AUTHOR) handong.li@ucl.ac.uk, Dai, Xuewu2 (AUTHOR) xuewu.dai@northumbria.ac.uk, Goldrick, Stephen1 (AUTHOR), Kotter, Richard2 (AUTHOR) richard.kotter@northumbria.ac.uk, Aslam, Nauman2 (AUTHOR) nauman.aslam@northumbria.ac.uk, Ali, Saleh3 (AUTHOR) saleh.ali@ncl.ac.uk
Publikováno v:
Energies (19961073). Nov2024, Vol. 17 Issue 21, p5442. 21p.
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