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Video temporal character grouping locates appearing moments of majorcharacters within a video according to their identities. To this end, recentworks have evolved from unsupervised clustering to graph-based supervisedclustering. However, graph methods are built upon the premise of fixed affinitygraphs, bringing many inexact connections. Besides, they extract multi-modalfeatures with kinds of models, which are unfriendly to deployment. In thispaper, we present a unified and dynamic graph (UniDG) framework for temporalcharacter grouping. This is accomplished firstly by a unified representationnetwork that learns representations of multiple modalities within the samespace and still preserves the modality's uniqueness simultaneously. Secondly,we present a dynamic graph clustering where the neighbors of differentquantities are dynamically constructed for each node via a cyclic matchingstrategy, leading to a more reliable affinity graph. Thirdly, a progressiveassociation method is introduced to exploit spatial and temporal contexts amongdifferent modalities, allowing multi-modal clustering results to be well fused.As current datasets only provide pre-extracted features, we evaluate our UniDGmethod on a collected dataset named MTCG, which contains each character'sappearing clips of face and body and speaking voice tracks. We also evaluateour key components on existing clustering and retrieval datasets to verify thegeneralization ability. Experimental results manifest that our method canachieve promising results and outperform several state-of-the-art approaches.

Unified and Dynamic Graph for Temporal Character Grouping in Long Videos