Papers › Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation

Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation

1 Sep 2018ECCV 2018 9archive 2025-07-28

Ruochen Fan, Qibin Hou, Ming-Ming Cheng, Gang Yu, Ralph R. Martin, Shi-Min Hu

Effectively bridging between image level keyword annotations and corresponding image pixels is one of the main challenges in weakly supervised semantic segmentation. In this paper, we use an instance-level salient object detector to automatically generate salient instances (candidate objects) for training images. Using similarity features extracted from each salient instance in the whole training set, we build a similarity graph, then use a graph partitioning algorithm to separate it into multiple subgraphs, each of which is associated with a single keyword (tag). Our graph-partitioning-based clustering algorithm allows us to consider the relationships between all salient instances in the training set as well as the information within them. We further show that with the help of attention information, our clustering algorithm is able to correct certain wrong assignments, leading to more accurate results. The proposed framework is general, and any state-of-the-art fully-supervised network structure can be incorporated to learn the segmentation network. When working with DeepLab for semantic segmentation, our method outperforms state-of-the-art weakly supervised alternatives by a large margin, achieving 65.6% mIoU on the PASCAL VOC 2012 dataset. We also combine our method with Mask R-CNN for instance segmentation, and demonstrated for the first time the ability of weakly supervised instance segmentation using only keyword annotations.

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Tasks

Clustering Image-level Supervised Instance Segmentation Instance Segmentation Segmentation Semantic Segmentation TAG Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation Weakly-supervised instance segmentation graph partitioning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image-level Supervised Instance Segmentation	COCO test-dev	Fan et al.	AP	13.7	#6 of 7	Archive leaderboard	report
Image-level Supervised Instance Segmentation	COCO test-dev	Fan et al.	AP@50	25.5	#6 of 7	Archive leaderboard	report
Image-level Supervised Instance Segmentation	COCO test-dev	Fan et al.	AP@75	13.5	#6 of 7	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.

Methods

CRF Convolution DeepLab Dense Connections Dilated Convolution Feedforward Network Mask R-CNN RPN RoIAlign Softmax

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