  Violence detection has been studied in computer vision for years. However,
previous work are either superficial, e.g., classification of short-clips, and
the single scenario, or undersupplied, e.g., the single modality, and
hand-crafted features based multimodality. To address this problem, in this
work we first release a large-scale and multi-scene dataset named XD-Violence
with a total duration of 217 hours, containing 4754 untrimmed videos with audio
signals and weak labels. Then we propose a neural network containing three
parallel branches to capture different relations among video snippets and
integrate features, where holistic branch captures long-range dependencies
using similarity prior, localized branch captures local positional relation
using proximity prior, and score branch dynamically captures the closeness of
predicted score. Besides, our method also includes an approximator to meet the
needs of online detection. Our method outperforms other state-of-the-art
methods on our released dataset and other existing benchmark. Moreover,
extensive experimental results also show the positive effect of multimodal
(audio-visual) input and modeling relationships. The code and dataset will be
released in https://roc-ng.github.io/XD-Violence/.
