Machine learningDeep learning / NLP / CV

Weakly Supervised Instance Segmentation

Weakly supervised instance segmentation trains deep networks to delineate individual object instances at pixel level using only cheap, incomplete annotations — such as bounding boxes, image-level labels, or point clicks — rather than costly full pixel-wise masks. It dramatically reduces annotation effort while still producing instance-level masks for each object in an image.

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Sources

  1. Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., & Chuang, Y.-Y. (2019). Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems (NeurIPS), 32. link
  2. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929. DOI: 10.1109/CVPR.2016.319

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Referenced by

ScholarGateWeakly Supervised Instance Segmentation (Weakly Supervised Instance Segmentation (Deep Learning with Incomplete Annotations)). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/weakly-supervised-instance-segmentation