Multi-Level Temporal Pyramid Network for Action Detection

• URL: http://arxiv.org/abs/2008.03270v1
• Date: Fri, 7 Aug 2020 17:08:24 GMT
• Title: Multi-Level Temporal Pyramid Network for Action Detection
• Authors: Xiang Wang, Changxin Gao, Shiwei Zhang, and Nong Sang
• Abstract summary: We propose a Multi-Level Temporal Network (MN) to improve the discrimination of the features. By this means, the proposed MN can learn rich and discriminative features for different action instances with different durations. We evaluate MN on two challenging datasets: THUMOS'14 and Activitynet v1.3, and the experimental results show that MN obtains competitive performance on Activitynet v1.3 and outperforms the state-of-the-art approaches on THUMOS'14 significantly.
• Score: 47.223376232616424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, one-stage frameworks have been widely applied for temporal action detection, but they still suffer from the challenge that the action instances span a wide range of time. The reason is that these one-stage detectors, e.g., Single Shot Multi-Box Detector (SSD), extract temporal features only applying a single-level layer for each head, which is not discriminative enough to perform classification and regression. In this paper, we propose a Multi-Level Temporal Pyramid Network (MLTPN) to improve the discrimination of the features. Specially, we first fuse the features from multiple layers with different temporal resolutions, to encode multi-layer temporal information. We then apply a multi-level feature pyramid architecture on the features to enhance their discriminative abilities. Finally, we design a simple yet effective feature fusion module to fuse the multi-level multi-scale features. By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations. We evaluate MLTPN on two challenging datasets: THUMOS'14 and Activitynet v1.3, and the experimental results show that MLTPN obtains competitive performance on Activitynet v1.3 and outperforms the state-of-the-art approaches on THUMOS'14 significantly.

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