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Multi-modal Transformers Excel at Class-agnostic Object Detection. (arXiv:2111.11430v2 [cs.CV] UPDATED)
Jan. 21, 2022, 2:10 a.m. | Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Ming-Hsuan Yang
cs.CV updates on arXiv.org arxiv.org
What constitutes an object? This has been a long-standing question in
computer vision. Towards this goal, numerous learning-free and learning-based
approaches have been developed to score objectness. However, they generally do
not scale well across new domains and for unseen objects. In this paper, we
advocate that existing methods lack a top-down supervision signal governed by
human-understandable semantics. To bridge this gap, we explore recent
Multi-modal Vision Transformers (MViT) that have been trained with aligned
image-text pairs. Our extensive experiments …
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