April 5, 2024, 4:45 a.m. | Taichi Nishimura, Koki Yamamoto, Yuto Haneji, Keiya Kajimura, Chihiro Nishiwaki, Eriko Daikoku, Natsuko Okuda, Fumihito Ono, Hirotaka Kameko, Shinsuke

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.03161v1 Announce Type: new
Abstract: This paper introduces a biochemical vision-and-language dataset, which consists of 24 egocentric experiment videos, corresponding protocols, and video-and-language alignments. The key challenge in the wet-lab domain is detecting equipment, reagents, and containers is difficult because the lab environment is scattered by filling objects on the table and some objects are indistinguishable. Therefore, previous studies assume that objects are manually annotated and given for downstream tasks, but this is costly and time-consuming. To address this issue, …

abstract arxiv challenge containers cs.cl cs.cv cs.mm dataset domain environment equipment experiment key lab language micro objects paper qr codes table the key type video videos vision vision-and-language

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