  Weakly-supervised temporal action localization (WTAL) is a practical yet
challenging task. Due to large-scale datasets, most existing methods use a
network pretrained in other datasets to extract features, which are not
suitable enough for WTAL. To address this problem, researchers design several
modules for feature enhancement, which improve the performance of the
localization module, especially modeling the temporal relationship between
snippets. However, all of them neglect the adverse effects of ambiguous
information, which would reduce the discriminability of others. Considering
this phenomenon, we propose Discriminability-Driven Graph Network (DDG-Net),
which explicitly models ambiguous snippets and discriminative snippets with
well-designed connections, preventing the transmission of ambiguous information
and enhancing the discriminability of snippet-level representations.
Additionally, we propose feature consistency loss to prevent the assimilation
of features and drive the graph convolution network to generate more
discriminative representations. Extensive experiments on THUMOS14 and
ActivityNet1.2 benchmarks demonstrate the effectiveness of DDG-Net,
establishing new state-of-the-art results on both datasets. Source code is
available at \url{https://github.com/XiaojunTang22/ICCV2023-DDGNet}.
