  Temporal Activity Detection aims to predict activity classes per frame, in
contrast to video-level predictions in Activity Classification (i.e., Activity
Recognition). Due to the expensive frame-level annotations required for
detection, the scale of detection datasets is limited. Thus, commonly, previous
work on temporal activity detection resorts to fine-tuning a classification
model pretrained on large-scale classification datasets (e.g., Kinetics-400).
However, such pretrained models are not ideal for downstream detection, due to
the disparity between the pretraining and the downstream fine-tuning tasks. In
this work, we propose a novel 'weakly-guided self-supervised' pretraining
method for detection. We leverage weak labels (classification) to introduce a
self-supervised pretext task (detection) by generating frame-level pseudo
labels, multi-action frames, and action segments. Simply put, we design a
detection task similar to downstream, on large-scale classification data,
without extra annotations. We show that the models pretrained with the proposed
weakly-guided self-supervised detection task outperform prior work on multiple
challenging activity detection benchmarks, including Charades and MultiTHUMOS.
Our extensive ablations further provide insights on when and how to use the
proposed models for activity detection. Code is available at
https://github.com/kkahatapitiya/SSDet.
