May 8, 2024, 4:46 a.m. | Melissa Hall, Samuel J. Bell, Candace Ross, Adina Williams, Michal Drozdzal, Adriana Romero Soriano

cs.CV updates on arXiv.org arxiv.org

arXiv:2405.04457v1 Announce Type: new
Abstract: Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of thoroughly evaluating their performance and identifying potential biases. In pursuit of models that generate images that are realistic, diverse, visually appealing, and consistent with the given prompt, researchers and practitioners often turn to automated metrics to facilitate scalable and cost-effective performance profiling. However, commonly-used metrics often fail to account for the full diversity of human preference; …

abstract arxiv biases consistent cs.cv cs.cy cs.hc deployment diverse evaluation generate generative generative models image images importance inclusion performance progress prompt text text-to-image type visual

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

AI Engineer

@ Holcim Group | Navi Mumbai, MH, IN, 400708