Papers › Refining Action Segmentation With Hierarchical Video Representations

Refining Action Segmentation With Hierarchical Video Representations

1 Jan 2021ICCV 2021 10archive 2025-07-28

Hyemin Ahn, Dongheui Lee

In this paper, we propose Hierarchical Action Segmentation Refiner (HASR), which can refine temporal action segmentation results from various models by understanding the overall context of a given video in a hierarchical way. When a backbone model for action segmentation estimates how the given video can be segmented, our model extracts segment-level representations based on frame-level features, and extracts a video-level representation based on the segment-level representations. Based on these hierarchical representations, our model can refer to the overall context of the entire video, and predict how the segment labels that are out of context should be corrected. Our HASR can be plugged into various action segmentation models (MS-TCN, SSTDA, ASRF), and improve the performance of state-of-the-art models based on three challenging datasets (GTEA, 50Salads, and Breakfast). For example, in 50Salads dataset, the segmental edit score improves from 67.9% to 77.4% (MS-TCN), from 75.8% to 77.3% (SSTDA), from 79.3% to 81.0% (ASRF). In addition, our model can refine the segmentation result from the unseen backbone model, which was not referred to when training HASR. This generalization performance would make HASR be an effective tool for boosting up the existing approaches for temporal action segmentation. Our code is available at https://github.com/cotton-ahn/HASR_iccv2021.

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Code

cotton-ahn/hasr_iccv2021 officialmentioned in paperpytorchMIT report

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Tasks

Action SegmentationSegmentationTemporal Action Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Segmentation 50 Salads ASRF + HASR Acc 83.9 #13 of 28 Archive leaderboard report
Action Segmentation 50 Salads ASRF + HASR Edit 81.0 #13 of 28 Archive leaderboard report
Action Segmentation 50 Salads ASRF + HASR F1@10% 86.6 #13 of 28 Archive leaderboard report
Action Segmentation 50 Salads ASRF + HASR F1@25% 85.7 #13 of 28 Archive leaderboard report
Action Segmentation 50 Salads ASRF + HASR F1@50% 78.5 #13 of 28 Archive leaderboard report
Action Segmentation Breakfast ASRF + HASR Acc 69.4 #15 of 37 Archive leaderboard report
Action Segmentation Breakfast ASRF + HASR Average F1 67.1 #15 of 37 Archive leaderboard report
Action Segmentation Breakfast ASRF + HASR Edit 71.9 #15 of 37 Archive leaderboard report
Action Segmentation Breakfast ASRF + HASR F1@10% 74.7 #15 of 37 Archive leaderboard report
Action Segmentation Breakfast ASRF + HASR F1@25% 69.5 #15 of 37 Archive leaderboard report
Action Segmentation Breakfast ASRF + HASR F1@50% 57.0 #15 of 37 Archive leaderboard report
Action Segmentation GTEA SSTDA + HASR Acc 78.7 #17 of 28 Archive leaderboard report
Action Segmentation GTEA SSTDA + HASR Edit 87.5 #17 of 28 Archive leaderboard report
Action Segmentation GTEA SSTDA + HASR F1@10% 90.9 #17 of 28 Archive leaderboard report
Action Segmentation GTEA SSTDA + HASR F1@25% 88.6 #17 of 28 Archive leaderboard report
Action Segmentation GTEA SSTDA + HASR F1@50% 76.4 #17 of 28 Archive leaderboard report
Action Segmentation GTEA ASRF + HASR Acc 76.9 #21 of 28 Archive leaderboard report
Action Segmentation GTEA ASRF + HASR Edit 84.5 #21 of 28 Archive leaderboard report
Action Segmentation GTEA ASRF + HASR F1@10% 89.2 #21 of 28 Archive leaderboard report
Action Segmentation GTEA ASRF + HASR F1@25% 87.2 #21 of 28 Archive leaderboard report
Action Segmentation GTEA ASRF + HASR F1@50% 74.8 #21 of 28 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

SSTDA

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