mlsanityData to 7 Oct 2026

Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection

arXiv 2108.01785 ↗First posted 3 Aug 2021cs.CVcited by 1

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Abstract (arXiv)

Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy. Hence, weakly supervised tasks, including weakly supervised object localization~(WSOL) and detection~(WSOD), have recently received attention in the computer vision community. In this paper, we motivate and propose the weakly supervised foreground learning (WSFL) task by showing that both WSOL and WSOD can be greatly improved if groundtruth foreground masks are available. More importantly, we propose a complete WSFL pipeline with low computational cost, which generates pseudo boxes, learns foreground masks, and does not need any localization annotations. With the help of foreground masks predicted by our WSFL model, we achieve 72.97% correct localization accuracy on CUB for WSOL, and 55.7% mean average precision on VOC07 for WSOD, thereby establish new state-of-the-art for both tasks. Our WSFL model also shows excellent transfer ability.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 48 references that are in the map. First posted on arXiv 3 Aug 2021.

Builds on

30 of 48 references.

References · 30

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18 more
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Open-World Weakly-Supervised Object Localizationcited by 2