Maximization and Restoration: Action Segmentation through Dilation Passing and Temporal Reconstruction

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 63
  • Download : 0
Action segmentation aims to split videos into segments of different actions. Recent work focuses on dealing with long-range dependencies of long, untrimmed videos, but still suffers from over-segmentation and performance saturation due to increased model complexity. This paper addresses the aforementioned issues through a divide-and-conquer strategy that first maximizes the frame-wise classification accuracy of the model and then reduces the over-segmentation errors. This strategy is implemented with the Dilation Passing and Reconstruction Network, composed of the Dilation Passing Network, which primarily aims to increase accuracy by propagating information of different dilations, and the Temporal Reconstruction Network, which reduces over-segmentation errors by temporally encoding and decoding the output features from the Dilation Passing Network. We also propose a weighted temporal mean squared error loss that further reduces over-segmentation. Through evaluations on the 50Salads, GTEA, and Breakfast datasets, we show that our model achieves significant results compared to existing state-of-the-art models.
Publisher
ELSEVIER SCI LTD
Issue Date
2022-09
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION, v.129

ISSN
0031-3203
DOI
10.1016/j.patcog.2022.108764
URI
http://hdl.handle.net/10203/297366
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0