  This technical report presents our solution to the HACS Temporal Action
Localization Challenge 2021, Weakly-Supervised Learning Track. The goal of
weakly-supervised temporal action localization is to temporally locate and
classify action of interest in untrimmed videos given only video-level labels.
We adopt the two-stream consensus network (TSCN) as the main framework in this
challenge. The TSCN consists of a two-stream base model training procedure and
a pseudo ground truth learning procedure. The base model training encourages
the model to predict reliable predictions based on single modality (i.e., RGB
or optical flow), based on the fusion of which a pseudo ground truth is
generated and in turn used as supervision to train the base models. On the HACS
v1.1.1 dataset, without fine-tuning the feature-extraction I3D models, our
method achieves 22.20% on the validation set and 21.68% on the testing set in
terms of average mAP. Our solution ranked the 2nd in this challenge, and we
hope our method can serve as a baseline for future academic research.
