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pro vyhledávání: '"Chong, Wen Haw"'
Stance detection aims to identify the attitude expressed in a document towards a given target. Techniques such as Chain-of-Thought (CoT) prompting have advanced this task, enhancing a model's reasoning capabilities through the derivation of intermedi
Externí odkaz:
http://arxiv.org/abs/2308.16763
Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. How
Externí odkaz:
http://arxiv.org/abs/2308.08088
Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classi
Externí odkaz:
http://arxiv.org/abs/2305.17678
Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hate
Externí odkaz:
http://arxiv.org/abs/2302.04156
Hateful meme detection is a new multimodal task that has gained significant traction in academic and industry research communities. Recently, researchers have applied pre-trained visual-linguistic models to perform the multimodal classification task,
Externí odkaz:
http://arxiv.org/abs/2204.01734
Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful c
Externí odkaz:
http://arxiv.org/abs/2108.06207
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Akademický článek
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Publikováno v:
Advances in Information Retrieval 37th European Conference on IR Research, ECIR 2015, Vienna, Austria, March 29 - April 2, 2015. Proceedings; 2015, p623-634, 12p
Publikováno v:
Influence of Technology on Social Network Analysis & Mining; 2013, p255-273, 19p