Semi-Supervised Temporal Action Detection with Proposal-Free Masking

• URL: http://arxiv.org/abs/2207.07059v1
• Date: Thu, 14 Jul 2022 16:58:47 GMT
• Title: Semi-Supervised Temporal Action Detection with Proposal-Free Masking
• Authors: Sauradip Nag, Xiatian Zhu, Yi-Zhe Song and Tao Xiang
• Abstract summary: We propose a novel Semi-supervised Temporal action detection model based on PropOsal-free Temporal mask (SPOT) SPOT outperforms state-of-the-art alternatives, often by a large margin.
• Score: 134.26292288193298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing temporal action detection (TAD) methods rely on a large number of training data with segment-level annotations. Collecting and annotating such a training set is thus highly expensive and unscalable. Semi-supervised TAD (SS-TAD) alleviates this problem by leveraging unlabeled videos freely available at scale. However, SS-TAD is also a much more challenging problem than supervised TAD, and consequently much under-studied. Prior SS-TAD methods directly combine an existing proposal-based TAD method and a SSL method. Due to their sequential localization (e.g, proposal generation) and classification design, they are prone to proposal error propagation. To overcome this limitation, in this work we propose a novel Semi-supervised Temporal action detection model based on PropOsal-free Temporal mask (SPOT) with a parallel localization (mask generation) and classification architecture. Such a novel design effectively eliminates the dependence between localization and classification by cutting off the route for error propagation in-between. We further introduce an interaction mechanism between classification and localization for prediction refinement, and a new pretext task for self-supervised model pre-training. Extensive experiments on two standard benchmarks show that our SPOT outperforms state-of-the-art alternatives, often by a large margin. The PyTorch implementation of SPOT is available at https://github.com/sauradip/SPOT

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