We propose an unsupervised method for reference resolution in instructional videos, where the goal is to temporally link an entity (e.g., "dressing") to the action (e.g., "mix yogurt") that produced it. The key challenge is the inevitable visual-linguistic ambiguities arising from the changes in both visual appearance and referring expression of an entity in the video. This challenge is amplified by the fact that we aim to resolve references with no supervision. We address these challenges by learning a joint visual-linguistic model, where linguistic cues can help resolve visual ambiguities and vice versa. We verify our approach by learning our model unsupervisedly using more than two thousand unstructured cooking videos from YouTube, and show that our visual-linguistic model can substantially improve upon state-of-the-art linguistic only model on reference resolution in instructional videos.


The dataset will be released here.


  title={Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos},
  author={Huang, De-An and Lim, Joseph J. and Fei-Fei, Li and Niebles, Juan Carlos},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

Example Results


This research was sponsored in part by grants from the Stanford AI Lab-Toyota Center for Artificial Intelligence Research, the Office of Naval Research (N00014-15-1-2813), and the ONR MURI (N00014-16-1-2127).