Papers › BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation

BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation

16 Mar 2021CVPR 2021 1arXiv:2103.08907archive 2025-07-28

Jungbeom Lee, Jihun Yi, Chaehun Shin, Sungroh Yoon

Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level information from the behavior of a trained object detector, by seeking the smallest areas of the image from which the object detector produces almost the same result as it does from the whole image. These areas constitute a bounding-box attribution map (BBAM), which identifies the target object in its bounding box and thus serves as pseudo ground-truth for weakly supervised semantic and instance segmentation. This approach significantly outperforms recent comparable techniques on both the PASCAL VOC and MS COCO benchmarks in weakly supervised semantic and instance segmentation. In addition, we provide a detailed analysis of our method, offering deeper insight into the behavior of the BBAM.

PaperPDFConference PDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

jbeomlee93/BBAM officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Box-supervised Instance SegmentationInstance SegmentationObjectSegmentationSemantic SegmentationWeakly supervised segmentationWeakly-Supervised Semantic SegmentationWeakly-supervised instance segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Box-supervised Instance Segmentation COCO test-dev BBAM mask AP 25.7 #6 of 7 Archive leaderboard report
Box-supervised Instance Segmentation PASCAL VOC 2012 val BBAM AP_25 76.8 #4 of 5 Archive leaderboard report
Box-supervised Instance Segmentation PASCAL VOC 2012 val BBAM AP_50 63.7 #4 of 5 Archive leaderboard report
Box-supervised Instance Segmentation PASCAL VOC 2012 val BBAM AP_70 39.5 #4 of 5 Archive leaderboard report
Box-supervised Instance Segmentation PASCAL VOC 2012 val BBAM AP_75 31.8 #4 of 5 Archive leaderboard report
Weakly-supervised instance segmentation PASCAL VOC 2012 val BBAM Average Best Overlap 63.0 #1 of 6 Archive leaderboard report
Weakly-supervised instance segmentation PASCAL VOC 2012 val BBAM mAP@0.25 76.8 #1 of 6 Archive leaderboard report
Weakly-supervised instance segmentation PASCAL VOC 2012 val BBAM mAP@0.5 63.7 #1 of 6 Archive leaderboard report
Weakly-supervised instance segmentation PASCAL VOC 2012 val BBAM mAP@0.75 31.8 #1 of 6 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.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections