The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs
Abstract
Action recognition has typically treated actions and activities as monolithic events that occur in videos. However, there is evidence from Cognitive Science and Neuroscience that people actively encode activities into consistent hierarchical part structures. However, in Computer Vision, few explorations on representations that encode event partonomies have been made. Inspired by evidence that the prototypical unit of an event is an action-object interaction, we introduce Action Genome, a representation that decomposes actions into spatio-temporal scene graphs. Action Genome captures changes between objects and their pairwise relationships while an action occurs. It contains 10K videos with 0.4M objects and 1.7M visual relationships annotated. With Action Genome, we extend an existing action recognition model by incorporating scene graphs as spatio-temporal feature banks to achieve better performance on the Charades dataset. Next, by decomposing and learning the temporal changes in visual relationships that result in an action, we demonstrate the utility of a hierarchical event decomposition by enabling few-shot action recognition, achieving 42.7% mAP using as few as 10 examples. Finally, we benchmark existing scene graph models on the new task of spatio-temporal scene graph prediction.
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Self-citationDeep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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Learning to Generate Scene Graph from Natural Language Supervision
Zhong, Y., Shi, J., Yang, J., Xu, C., & Li, Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00184 · Cited by 73
Target Adaptive Context Aggregation for Video Scene Graph Generation
Teng, Y., Wang, L., Li, Z., & Wu, G. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01343 · Cited by 74
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers
Geng, S., Gao, P., Chatterjee, M., Hori, C., Le Roux, J., Zhang, Y., Li, H., & Cherian, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16231 · Cited by 38
Scene Graph Contrastive Learning for Embodied Navigation
Singh, K. P., Salvador, J., Weihs, L., & Kembhavi, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00999 · Cited by 23
Metadata
- Authors
- Ji, J., Krishna, R., Fei-Fei, L., & Niebles, J. C.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10233-10244
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034679267
- Cited by
- 303
- References
- 89
- FWCI
- 14.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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