Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos

• URL: http://arxiv.org/abs/2110.05697v1
• Date: Tue, 12 Oct 2021 02:32:15 GMT
• Title: Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional Videos
• Authors: Reza Ghoddoosian, Saif Sayed, Vassilis Athitsos
• Abstract summary: This paper focuses on task recognition and action segmentation in weakly-labeled instructional videos. We propose a two-stream framework, which exploits semantic and temporal hierarchies to recognize top-level tasks in instructional videos. We present a novel top-down weakly-supervised action segmentation approach, where the predicted task is used to constrain the inference of fine-grained action sequences.
• Score: 6.187780920448871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on task recognition and action segmentation in weakly-labeled instructional videos, where only the ordered sequence of video-level actions is available during training. We propose a two-stream framework, which exploits semantic and temporal hierarchies to recognize top-level tasks in instructional videos. Further, we present a novel top-down weakly-supervised action segmentation approach, where the predicted task is used to constrain the inference of fine-grained action sequences. Experimental results on the popular Breakfast and Cooking 2 datasets show that our two-stream hierarchical task modeling significantly outperforms existing methods in top-level task recognition for all datasets and metrics. Additionally, using our task recognition framework in the proposed top-down action segmentation approach consistently improves the state of the art, while also reducing segmentation inference time by 80-90 percent.

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