Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Ogezi, Michael"'
Autor:
Ogezi, Michael, Shi, Ning
In text-to-image generation, using negative prompts, which describe undesirable image characteristics, can significantly boost image quality. However, producing good negative prompts is manual and tedious. To address this, we propose NegOpt, a novel
Externí odkaz:
http://arxiv.org/abs/2403.07605
We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Fu
Externí odkaz:
http://arxiv.org/abs/2306.14067
Language-vision models like CLIP have made significant strides in vision tasks, such as zero-shot image classification (ZSIC). However, generating specific and expressive visual descriptions remains challenging; descriptions produced by current metho
Externí odkaz:
http://arxiv.org/abs/2306.06077
Language-vision models like CLIP have made significant progress in zero-shot vision tasks, such as zero-shot image classification (ZSIC). However, generating specific and expressive class descriptions remains a major challenge. Existing approaches su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eff10e83663ee40c0965eb6b5b426026