  This technical report presents an overview of our solution used in the
submission to 2021 HACS Temporal Action Localization Challenge on both
Supervised Learning Track and Weakly-Supervised Learning Track. Temporal Action
Localization (TAL) requires to not only precisely locate the temporal
boundaries of action instances, but also accurately classify the untrimmed
videos into specific categories. However, Weakly-Supervised TAL indicates
locating the action instances using only video-level class labels. In this
paper, to train a supervised temporal action localizer, we adopt Temporal
Context Aggregation Network (TCANet) to generate high-quality action proposals
through ``local and global" temporal context aggregation and complementary as
well as progressive boundary refinement. As for the WSTAL, a novel framework is
proposed to handle the poor quality of CAS generated by simple classification
network, which can only focus on local discriminative parts, rather than locate
the entire interval of target actions. Further inspired by the transfer
learning method, we also adopt an additional module to transfer the knowledge
from trimmed videos (HACS Clips dataset) to untrimmed videos (HACS Segments
dataset), aiming at promoting the classification performance on untrimmed
videos. Finally, we employ a boundary regression module embedded with
Outer-Inner-Contrastive (OIC) loss to automatically predict the boundaries
based on the enhanced CAS. Our proposed scheme achieves 39.91 and 29.78 average
mAP on the challenge testing set of supervised and weakly-supervised temporal
action localization track respectively.
