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Illegal live-streaming identification, which aims to help live-streamingplatforms immediately recognize the illegal behaviors in the live-streaming,such as selling precious and endangered animals, plays a crucial role inpurifying the network environment. Traditionally, the live-streaming platformneeds to employ some professionals to manually identify the potential illegallive-streaming. Specifically, the professional needs to search for relatedevidence from a large-scale knowledge database for evaluating whether a givenlive-streaming clip contains illegal behavior, which is time-consuming andlaborious. To address this issue, in this work, we propose a multimodalevidence retrieval system, named OFAR, to facilitate the illegal live-streamingidentification. OFAR consists of three modules: Query Encoder, DocumentEncoder, and MaxSim-based Contrastive Late Intersection. Both query encoder anddocument encoder are implemented with the advanced OFA encoder, which ispretrained on a large-scale multimodal dataset. In the last module, weintroduce contrastive learning on the basis of the MaxiSim-based lateintersection, to enhance the model's ability of query-document matching. Theproposed framework achieves significant improvement on our industrial datasetTaoLive, demonstrating the advances of our scheme.

OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification