  Weakly-supervised temporal action localization aims to localize and recognize
actions in untrimmed videos with only video-level category labels during
training. Without instance-level annotations, most existing methods follow the
Segment-based Multiple Instance Learning (S-MIL) framework, where the
predictions of segments are supervised by the labels of videos. However, the
objective for acquiring segment-level scores during training is not consistent
with the target for acquiring proposal-level scores during testing, leading to
suboptimal results. To deal with this problem, we propose a novel
Proposal-based Multiple Instance Learning (P-MIL) framework that directly
classifies the candidate proposals in both the training and testing stages,
which includes three key designs: 1) a surrounding contrastive feature
extraction module to suppress the discriminative short proposals by considering
the surrounding contrastive information, 2) a proposal completeness evaluation
module to inhibit the low-quality proposals with the guidance of the
completeness pseudo labels, and 3) an instance-level rank consistency loss to
achieve robust detection by leveraging the complementarity of RGB and FLOW
modalities. Extensive experimental results on two challenging benchmarks
including THUMOS14 and ActivityNet demonstrate the superior performance of our
method.
