BIT: Bi-Level Temporal Modeling for Efficient Supervised Action Segmentation

TL;DR

The paper introduces BIT, an efficient bi-level temporal modeling framework for supervised action segmentation that captures long-range dependencies with lower computational cost and leverages textual transcripts when available.

Contribution

It proposes a novel bi-level architecture with explicit action tokens and cross-attention, enabling efficient long-range modeling and transcript integration for action segmentation.

Findings

Achieves state-of-the-art accuracy on four datasets.

Runs 30 times faster than existing transformer-based methods.

Effectively leverages textual transcripts to improve segmentation.

Abstract

We address the task of supervised action segmentation which aims to partition a video into non-overlapping segments, each representing a different action. Recent works apply transformers to perform temporal modeling at the frame-level, which suffer from high computational cost and cannot well capture action dependencies over long temporal horizons. To address these issues, we propose an efficient BI-level Temporal modeling (BIT) framework that learns explicit action tokens to represent action segments, in parallel performs temporal modeling on frame and action levels, while maintaining a low computational cost. Our model contains (i) a frame branch that uses convolution to learn frame-level relationships, (ii) an action branch that uses transformer to learn action-level dependencies with a small set of action tokens and (iii) cross-attentions to allow communication between the two branches. We apply and extend a set-prediction objective to allow each action token to represent one or multiple action segments, thus can avoid learning a large number of tokens over long videos with many segments. Thanks to the design of our action branch, we can also seamlessly leverage textual transcripts of videos (when available) to help action segmentation by using them to initialize the action tokens. We evaluate our model on four video datasets (two egocentric and two third-person) for action segmentation with and without transcripts, showing that BIT significantly improves the state-of-the-art accuracy with much lower computational cost (30 times faster) compared to existing transformer-based methods.