Papers › Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior

Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior

1 Dec 2019NeurIPS 2019 12archive 2025-07-28

Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Yung-Yu Chuang

This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the problem as a multiple instance learning (MIL) task, and generate positive and negative bags based on the sweeping lines of each bounding box. The proposed deep model integrates MIL into a fully supervised instance segmentation network, and can be derived by the objective consisting of two terms, i.e., the unary term and the pairwise term. The former estimates the foreground and background areas of each bounding box while the latter maintains the unity of the estimated object masks. The experimental results show that our method performs favorably against existing weakly supervised methods and even surpasses some fully supervised methods for instance segmentation on the PASCAL VOC dataset.

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chengchunhsu/WSIS_BBTP officialmentioned in paperpytorch report

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Tasks

Box-supervised Instance Segmentation Instance Segmentation Multiple Instance Learning Segmentation Semantic Segmentation Unity Weakly-supervised instance segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Box-supervised Instance Segmentation	COCO test-dev	BBTP	mask AP	21.1	#7 of 7	Archive leaderboard	report
Box-supervised Instance Segmentation	PASCAL VOC 2012 val	BBTP	AP_50	59.1	#3 of 5	Archive leaderboard	report
Box-supervised Instance Segmentation	PASCAL VOC 2012 val	BBTP	AP_75	21.9	#3 of 5	Archive leaderboard	report
Box-supervised Instance Segmentation	PASCAL VOC 2012 val	BBTP	mask AP	27.5	#3 of 5	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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