Feb. 14, 2024, 5:46 a.m. | Michael Dorkenwald Nimrod Barazani Cees G. M. Snoek Yuki M. Asano

cs.CV updates on arXiv.org arxiv.org

Vision-Language Models (VLMs), such as Flamingo and GPT-4V, have shown immense potential by integrating large language models with vision systems. Nevertheless, these models face challenges in the fundamental computer vision task of object localisation, due to their training on multimodal data containing mostly captions without explicit spatial grounding. While it is possible to construct custom, supervised training pipelines with bounding box annotations that integrate with VLMs, these result in specialized and hard-to-scale models. In this paper, we aim to explore …

captions challenges computer computer vision cs.cv data face gpt gpt-4v language language models large language large language models multimodal multimodal data pin spatial systems training vision vision-language models vlms

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