The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
G-TAD: Sub-Graph Localization for Temporal Action Detection
Open accessAbstract
Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focus on temporal context, while neglecting semantic context as well as other important context properties. In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem. Specifically, we formulate video snippets as graph nodes, snippet-snippet correlations as edges, and actions associated with context as target sub-graphs. With graph convolution as the basic operation, we design a GCN block called GCNeXt, which learns the features of each node by aggregating its context and dynamically updates the edges in the graph. To localize each sub-graph, we also design an SGAlign layer to embed each sub-graph into the Euclidean space. Extensive experiments show that G-TAD is capable of finding effective video context without extra supervision and achieves state-of-the-art performance on two detection benchmarks. On ActivityNet-1.3 it obtains an average mAP of 34.09%; on THUMOS14 it reaches 51.6% at IoU@0.5 when combined with a proposal processing method. The code has been made available at https://github.com/frostinassiky/gtad.
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Cites
Deep Residual Learning for Image Recognition
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Learning Spatiotemporal Features with 3D Convolutional Networks
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Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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Dynamic Graph CNN for Learning on Point Clouds
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Self-citationSoft-NMS — Improving Object Detection with One Line of Code
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“For post-processing, we apply Soft-NMS [3], where the threshld is 0.”
MethodInfluentialEnd-to-End Learning of Action Detection from Frame Glimpses in Videos
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Self-citationVideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
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Self-Supervised Learning by Cross-Modal Audio-Video Clustering
Alwassel, H., Mahajan, D., Korbar, B., Torresani, L., Ghanem, B., & Tran, D. (2019) · arXiv · doi:10.48550/arxiv.1911.12667 · Cited by 298
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Metadata
- Authors
- Xu, M., Zhao, C., Rojas, D. S., Thabet, A., & Ghanem, B.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10153-10162
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034623254
- Cited by
- 483
- References
- 13
- FWCI
- 24.74
- Type
- conference paper
- Language
- en
- Open access
- green
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