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 visuallinguistic 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.
Huang et al. REF focus on such ambiguities caused by the changing in visual appearance and referring expression, and aim to resolve references with no supervision.
Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science