Label-Efficient Learning

Getting strong performance from limited annotations, through semi-supervised, weakly-supervised, and active learning.

References

2025

  1. video-action.jpg
    OmViD: Omni-supervised active learning for video action detection
    Aayush Rana, Akash Kumar, Vibhav Vineet, and Yogesh S Rawat
    In IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2025

2024

  1. Semi-supervised active learning for video action detection
    Ayush Singh, Aayush J Rana, Akash Kumar, Shruti Vyas, and Yogesh Singh Rawat
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

2023

  1. Hybrid active learning via deep clustering for video action detection
    Aayush J Rana and Yogesh S Rawat
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

  1. End-to-End Semi-Supervised Learning for Video Action Detection
    Akash Kumar and Yogesh Singh Rawat
    In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2022
  2. Are all Frames Equal? Active Sparse Labeling for Video Action Detection
    Aayush Rana and Yogesh S Rawat
    In Advances in Neural Information Processing Systems, 2022

2021

  1. In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning
    Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, and Mubarak Shah
    In The International Conference on Learning Representations (ICLR), 2021