  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.
