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Ying_Weakly-Supervised_Object_Representation_Learning_for_Few-Shot_Semantic_Segmentation_WACV_2021_paper.pdf
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🔗 Source: openaccess.thecvf.com
📊 Size: 2.76 MB
👤 Author: Xiaowen Ying, Xin Li, Mooi Choo Chuah
⬇️ Downloads: 526

Summary

Training semantic segmentation models requires large, densely-annotated image datasets, which are costly to obtain. This paper tackles the few-shot semantic segmentation problem, aiming to perform image segmentation on unseen object categories with only one or a few support examples. The key to solving this problem lies in effectively utilizing object information from support examples to separate target objects from the background in a query image. Existing methods typically generate object-level representations by averaging local features in support images, but these representations are often noisy and less distinguishing. To address this issue, the authors propose an Object Representation Generator (ORG) module that can effectively aggregate local object features from support images and produce better object-level representations. The ORG module can be embedded into a network and trained end-to-end in a weakly-supervised fashion without extra human annotation. Experimental results on the Pascal-VOC and MS-COCO datasets show that the proposed approach achieves better performance compared to existing methods under both one-shot and five-shot settings.

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Description

Training semantic segmentation models requires large, densely-annotated image datasets, which are costly to obtain.

Technical Information

  • File Format: PDF
  • File Size: 2.76 MB
  • Pages: 10
  • Language: EN
  • Author: Xiaowen Ying, Xin Li, Mooi Choo Chuah
  • Total Downloads: 526
  • Last Updated: 22 hours ago

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