Abstract
Recognizing social relationships between multiple characters from videos can enable intelligent systems to serve human society better. Previous studies mainly focus on the still image to classify the relationships while ignoring the important data source of the video. With the prosperity of multimedia, the methods of video-based social relationship recognition gradually emerge. However, those methods either only focus on the logical reasoning between multiple characters or only on the direct interaction in each character pair. To that end, inspired by the rules of interpersonal social communication, we propose Overall-Distinctive GCN (OD-GCN) to recognize the relationships of multiple characters in the videos. Specifically, we first construct an overall-level character heterogeneous graph with two types of edges and rich node representation features to capture the implicit relationship of all characters. Then, we design an attention module to find mentioned nodes for each character pair from feature sequences fused with temporal information. Further, we build distinctive-level graphs to focus on the interaction between two characters. Finally, we integrate multimodal global features to classify relationships. We conduct the experiments on the MovieGraphs dataset and validate the effectiveness of our proposed model.
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Acknowledgments
This work is supported by the NSFC-General Technology Basic Research Joint Funds under Grant (U1936220), the National Natural Science Foundation of China under Grant (61972047), the National Key Research and Development Program of China (2018YFC0831500).
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Hu, Y., Cao, C., Li, F., Yan, C., Qi, J., Wu, B. (2023). Overall-Distinctive GCN for Social Relation Recognition on Videos. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_5
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