CVPR 2023 - Post-Processing Temporal Action Detection

In this episode we discuss Post-Processing Temporal Action Detection by Sauradip Nag, Xiatian Zhu, Yi-Zhe Song, Tao Xiang. The paper proposes a novel model-agnostic post-processing method, called Gaussian Approximated Post-processing (GAP), to improve the performance of Temporal Action Detection (TAD) methods without requiring model redesign and retraining. The existing TAD methods usually have a pre-processing step that temporally downsamples the video, leading to a reduction in inference resolution and negative impact on the TAD performance. GAP models the start and end points of action instances with a Gaussian distribution and enables temporal boundary inference at a sub-snippet level. Experimental results demonstrate that GAP consistently improves a wide variety of pre-trained off-the-shelf TAD models on the ActivityNet and THUMOS benchmarks.