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Spatial–Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation.

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Title: Spatial–Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation.
Authors: Pu, Tao1 (AUTHOR) putao3@mail2.sysu.edu.cn, Chen, Tianshui2 (AUTHOR) tianshuichen@gmail.com, Wu, Hefeng1 (AUTHOR) wuhefeng@mail.sysu.edu.cn, Lu, Yongyi2 (AUTHOR) yylu1989@gmail.com, Lin, Liang1 (AUTHOR) linliang@ieee.org
Source: IEEE Transactions on Image Processing. 2024, Vol. 33, p556-568. 13p.
Subjects: Knowledge representation (Information theory), Statistical correlation, Videos, Prior learning
Abstract: Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. It requires not only a comprehensive understanding of each object scattered on the whole scene but also a deep dive into their temporal motions and interactions. Inherently, object pairs and their relationships enjoy spatial co-occurrence correlations within each image and temporal consistency/transition correlations across different images, which can serve as prior knowledge to facilitate VidSGG model learning and inference. In this work, we propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism to learn more representative relationship representations. Specifically, we first learn spatial co-occurrence and temporal transition correlations in a statistical manner. Then, we design spatial and temporal knowledge-embedded layers that introduce the multi-head cross-attention mechanism to fully explore the interaction between visual representation and the knowledge to generate spatial- and temporal-embedded representations, respectively. Finally, we aggregate these representations for each subject-object pair to predict the final semantic labels and their relationships. Extensive experiments show that STKET outperforms current competing algorithms by a large margin, e.g., improving the mR@50 by 8.1%, 4.7%, and 2.1% on different settings over current algorithms. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. It requires not only a comprehensive understanding of each object scattered on the whole scene but also a deep dive into their temporal motions and interactions. Inherently, object pairs and their relationships enjoy spatial co-occurrence correlations within each image and temporal consistency/transition correlations across different images, which can serve as prior knowledge to facilitate VidSGG model learning and inference. In this work, we propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism to learn more representative relationship representations. Specifically, we first learn spatial co-occurrence and temporal transition correlations in a statistical manner. Then, we design spatial and temporal knowledge-embedded layers that introduce the multi-head cross-attention mechanism to fully explore the interaction between visual representation and the knowledge to generate spatial- and temporal-embedded representations, respectively. Finally, we aggregate these representations for each subject-object pair to predict the final semantic labels and their relationships. Extensive experiments show that STKET outperforms current competing algorithms by a large margin, e.g., improving the mR@50 by 8.1%, 4.7%, and 2.1% on different settings over current algorithms. [ABSTRACT FROM AUTHOR]
ISSN:10577149
DOI:10.1109/TIP.2023.3345652