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FIFA: Fast Inference Approximation for Action Segmentation

9 Aug 2021arXiv:2108.03894archive 2025-07-28

Yaser Souri, Yazan Abu Farha, Fabien Despinoy, Gianpiero Francesca, Juergen Gall

We introduce FIFA, a fast approximate inference method for action segmentation and alignment. Unlike previous approaches, FIFA does not rely on expensive dynamic programming for inference. Instead, it uses an approximate differentiable energy function that can be minimized using gradient-descent. FIFA is a general approach that can replace exact inference improving its speed by more than 5 times while maintaining its performance. FIFA is an anytime inference algorithm that provides a better speed vs. accuracy trade-off compared to exact inference. We apply FIFA on top of state-of-the-art approaches for weakly supervised action segmentation and alignment as well as fully supervised action segmentation. FIFA achieves state-of-the-art results on most metrics on two action segmentation datasets.

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Tasks

Action SegmentationSegmentationWeakly Supervised Action Segmentation (Transcript)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Segmentation Breakfast FIFA + MS-TCN Acc 68.6 #17 of 37 Archive leaderboard report
Action Segmentation Breakfast FIFA + MS-TCN Average F1 66.8 #17 of 37 Archive leaderboard report
Action Segmentation Breakfast FIFA + MS-TCN Edit 78.5 #17 of 37 Archive leaderboard report
Action Segmentation Breakfast FIFA + MS-TCN F1@10% 75.5 #17 of 37 Archive leaderboard report
Action Segmentation Breakfast FIFA + MS-TCN F1@25% 70.2 #17 of 37 Archive leaderboard report
Action Segmentation Breakfast FIFA + MS-TCN F1@50% 54.8 #17 of 37 Archive leaderboard report
Weakly Supervised Action Segmentation (Transcript) Breakfast FIFA + MuCon Acc 51.3 #2 of 7 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.

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