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pro vyhledávání: '"Wu, Bohong"'
Image captioning has long been regarded as a fundamental task in visual understanding. Recently, however, few large vision-language model (LVLM) research discusses model's image captioning performance because of the outdated short-caption benchmarks
Externí odkaz:
http://arxiv.org/abs/2405.19092
Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner. Despite being simple and effective, this method results in sub-optimal cross-modal alignment by over-emphasizing the t
Externí odkaz:
http://arxiv.org/abs/2405.17871
Multilingual understanding models (or encoder-based), pre-trained via masked language modeling, have achieved promising results on many language understanding tasks (e.g., mBERT). However, these non-autoregressive (NAR) models still struggle to gener
Externí odkaz:
http://arxiv.org/abs/2305.13140
Autor:
Wu, Bohong, Zhao, Hai
Though offering amazing contextualized token-level representations, current pre-trained language models take less attention on accurately acquiring sentence-level representation during their self-supervised pre-training. However, contrastive objectiv
Externí odkaz:
http://arxiv.org/abs/2210.08474
Autor:
Wu, Bohong, Zhao, Hai
Though offering amazing contextualized token-level representations, current pre-trained language models actually take less attention on acquiring sentence-level representation during its self-supervised pre-training. If self-supervised learning can b
Externí odkaz:
http://arxiv.org/abs/2204.09358
Training dense passage representations via contrastive learning has been shown effective for Open-Domain Passage Retrieval (ODPR). Existing studies focus on further optimizing by improving negative sampling strategy or extra pretraining. However, the
Externí odkaz:
http://arxiv.org/abs/2110.07524
Multi-hop reading comprehension (MHRC) requires not only to predict the correct answer span in the given passage, but also to provide a chain of supporting evidences for reasoning interpretability. It is natural to model such a process into graph str
Externí odkaz:
http://arxiv.org/abs/2107.11823
Autor:
Hussein Khalaf, Ali, Xiao, Ying, Xu, Ning, Wu, Bohong, Li, Huan, Lin, Bing, Nie, Zhen, Tang, Junlei
Publikováno v:
In Engineering Failure Analysis January 2024 155
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