Papers › Importance Sampling CAMs for Weakly-Supervised Segmentation

Importance Sampling CAMs for Weakly-Supervised Segmentation

23 Mar 2022arXiv:2203.12459archive 2025-07-28

Arvi Jonnarth, Michael Felsberg

Classification networks can be used to localize and segment objects in images by means of class activation maps (CAMs). However, without pixel-level annotations, classification networks are known to (1) mainly focus on discriminative regions, and (2) to produce diffuse CAMs without well-defined prediction contours. In this work, we approach both problems with two contributions for improving CAM learning. First, we incorporate importance sampling based on the class-wise probability mass function induced by the CAMs to produce stochastic image-level class predictions. This results in CAMs which activate over a larger extent of objects. Second, we formulate a feature similarity loss term which aims to match the prediction contours with edges in the image. As a third contribution, we conduct experiments on the PASCAL VOC 2012 benchmark dataset to demonstrate that these modifications significantly increase the performance in terms of contour accuracy, while being comparable to current state-of-the-art methods in terms of region similarity.

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Code

arvijj/icam officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Semantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 test ICAM Mean IoU 70.8 #34 of 60 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 val ICAM Mean IoU 70.1 #44 of 73 Archive leaderboard report

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Methods

CAM

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