  Illegal live-streaming identification, which aims to help live-streaming
platforms immediately recognize the illegal behaviors in the live-streaming,
such as selling precious and endangered animals, plays a crucial role in
purifying the network environment. Traditionally, the live-streaming platform
needs to employ some professionals to manually identify the potential illegal
live-streaming. Specifically, the professional needs to search for related
evidence from a large-scale knowledge database for evaluating whether a given
live-streaming clip contains illegal behavior, which is time-consuming and
laborious. To address this issue, in this work, we propose a multimodal
evidence retrieval system, named OFAR, to facilitate the illegal live-streaming
identification. OFAR consists of three modules: Query Encoder, Document
Encoder, and MaxSim-based Contrastive Late Intersection. Both query encoder and
document encoder are implemented with the advanced OFA encoder, which is
pretrained on a large-scale multimodal dataset. In the last module, we
introduce contrastive learning on the basis of the MaxiSim-based late
intersection, to enhance the model's ability of query-document matching. The
proposed framework achieves significant improvement on our industrial dataset
TaoLive, demonstrating the advances of our scheme.
