  Weakly supervised temporal action localization is a newly emerging yet widely
studied topic in recent years. The existing methods can be categorized into two
localization-by-classification pipelines, i.e., the pre-classification pipeline
and the post-classification pipeline. The pre-classification pipeline first
performs classification on each video snippet and then aggregate the
snippet-level classification scores to obtain the video-level classification
score. In contrast, the post-classification pipeline aggregates the
snippet-level features first and then predicts the video-level classification
score based on the aggregated feature. Although the classifiers in these two
pipelines are used in different ways, the role they play is exactly the
same---to classify the given features to identify the corresponding action
categories. To this end, an ideal classifier can make both pipelines work. This
inspires us to simultaneously learn these two pipelines in a unified framework
to obtain an effective classifier. Specifically, in the proposed learning
framework, we implement two parallel network streams to model the two
localization-by-classification pipelines simultaneously and make the two
network streams share the same classifier. This achieves the novel Equivalent
Classification Mapping (ECM) mechanism. Moreover, we discover that an ideal
classifier may possess two characteristics: 1) The frame-level classification
scores obtained from the pre-classification stream and the feature aggregation
weights in the post-classification stream should be consistent; 2) The
classification results of these two streams should be identical. Based on these
two characteristics, we further introduce a weight-transition module and an
equivalent training strategy into the proposed learning framework, which
assists to thoroughly mine the equivalence mechanism.
