In vision-language models (VLMs), visual tokens usually consume a significant amount of computational overhead, despite their sparser information density compared to text tokens. To address this, ...
New research from the University of Kansas uses network science to determine why people make mistakes when lip-reading. Michael Vitevitch, professor of speech-language-hearing at KU, and his ...
Abstract: Pre-trained vision-language models (VLMs) and language models (LMs) have recently garnered significant attention due to their remarkable ability to represent textual concepts, opening up new ...
Abstract: Generating visual text in natural scene images is a challenging task with many unsolved problems. Different from generating text on artificially designed images (such as posters, covers, and ...
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