June 11, 2024, 4:42 a.m. | Leigang Qu, Haochuan Li, Tan Wang, Wenjie Wang, Yongqi Li, Liqiang Nie, Tat-Seng Chua

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.05814v1 Announce Type: cross
Abstract: How humans can efficiently and effectively acquire images has always been a perennial question. A typical solution is text-to-image retrieval from an existing database given the text query; however, the limited database typically lacks creativity. By contrast, recent breakthroughs in text-to-image generation have made it possible to produce fancy and diverse visual content, but it faces challenges in synthesizing knowledge-intensive images. In this work, we rethink the relationship between text-to-image generation and retrieval and propose …

abstract arxiv contrast creativity cs.ai cs.cl cs.cv cs.lg cs.mm database however humans image image generation images query question retrieval solution text text-to-image type

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