Abstract
Existing Temporal Action Detection (TAD) methods typ-
ically take a pre-processing step in converting an input
varying-length video into a fixed-length snippet represen-
tation sequence, before temporal boundary estimation and
action classification. This pre-processing step would tem-
porally downsample the video, reducing the inference res-
olution and hampering the detection performance in the
original temporal resolution. In essence, this is due to a
temporal quantization error introduced during the resolu-
tion downsampling and recovery. This could negatively im-
pact the TAD performance, but is largely ignored by existing
methods. To address this problem, in this work we intro-
duce a novel model-agnostic post-processing method with-
out model redesign and retraining. Specifically, we model
the start and end points of action instances with a Gaussian
distribution for enabling temporal boundary inference at a
sub-snippet level. We further introduce an efficient Taylor-
expansion based approximation, dubbed as Gaussian Ap-
proximated Post-processing (GAP). Extensive experiments
demonstrate that our GAP can consistently improve a wide
variety of pre-trained off-the-shelf TAD models on the chal-
lenging ActivityNet (+0.2%∼0.7% in average mAP) and
THUMOS (+0.2%∼0.5% in average mAP) benchmarks.
Such performance gains are already significant and highly
comparable to those achieved by novel model designs. Also,
GAP can be integrated with model training for further
performance gain. Importantly, GAP enables lower tem-
poral resolutions for more efficient inference, facilitating
low-resource applications. The code will be available in
https://github.com/sauradip/GAP