C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation

• URL: http://arxiv.org/abs/2212.11078v1
• Date: Tue, 20 Dec 2022 14:53:46 GMT
• Title: C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation
• Authors: Dipika Singhania, Rahul Rahaman, Angela Yao
• Abstract summary: Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. We propose an encoder-decoder-style architecture named C2F-TCN featuring a "coarse-to-fine" ensemble of decoder outputs. We show that the architecture is flexible for both supervised and representation learning.
• Score: 20.182928938110923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. For the task of temporal action segmentation, we propose an encoder-decoder-style architecture named C2F-TCN featuring a "coarse-to-fine" ensemble of decoder outputs. The C2F-TCN framework is enhanced with a novel model agnostic temporal feature augmentation strategy formed by the computationally inexpensive strategy of the stochastic max-pooling of segments. It produces more accurate and well-calibrated supervised results on three benchmark action segmentation datasets. We show that the architecture is flexible for both supervised and representation learning. In line with this, we present a novel unsupervised way to learn frame-wise representation from C2F-TCN. Our unsupervised learning approach hinges on the clustering capabilities of the input features and the formation of multi-resolution features from the decoder's implicit structure. Further, we provide the first semi-supervised temporal action segmentation results by merging representation learning with conventional supervised learning. Our semi-supervised learning scheme, called ``Iterative-Contrastive-Classify (ICC)'', progressively improves in performance with more labeled data. The ICC semi-supervised learning in C2F-TCN, with 40% labeled videos, performs similar to fully supervised counterparts.

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