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Papers › Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation

Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation

1 Jul 2019Proceedings of the AAAI Conference on Artificial Intelligence 2019 7archive 2025-07-28

Tao Hu, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, Cees G. M. Snoek

Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremen- dous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixel- level annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attention- based Multi-Context Guiding (A-MCG) network, which con- sists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between sup- port and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial atten- tion along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learn- ing, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% mea- sured in mIoU in the 1-shot and 5-shot setting.

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Tasks

Few-Shot LearningFew-Shot Semantic SegmentationOne-Shot LearningSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Semantic Segmentation Pascal5i A-MCG-Conv-LSTM meanIOU 62.2 #1 of 3 Archive leaderboard report

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