Research map / Video / Action recognition
I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| TS-GCN vs prior SOTA | ZSAR · UCF101 (inductive) | accuracy: 30.3 -> 34.2 | Helped |
| TS-GCN vs ZSECOC | ZSAR · Olympic Sports (inductive) | accuracy: 59.8 -> 56.5 | Hurt |
| add FV visual features to TS-GCN | TS-GCN · UCF101 (inductive) | accuracy: +2.1% | Helped |
| two-stream instead of one-stream GCN (ablation) | TS-GCN · UCF101 | accuracy: 25.2 -> 42.8 | Helped |
| add self-attention over video segments (ablation) | TS-GCN · UCF101 | accuracy: 39.6 -> 42.8 | Helped |
| YFCC100M word embeddings instead of GoogleNews (ablation) | TS-GCN · Olympic/HMDB51/UCF101 | accuracy: 60.5 -> 59.2 (Olympic); 40.5 -> 42.8 (UCF101) | Mixed |
| GCN depth beyond 3 layers (ablation) | TS-GCN · three ZSAR benchmarks | accuracy | No clear effect |
| moderate number of objects K per action (ablation) | TS-GCN · three ZSAR benchmarks | accuracy | Mixed |
Closest papers by meaning
- Reformulating Zero-shot Action Recognition for Multi-label Actions (NeurIPS 2021 · similarity 0.90)
- Cross-Modal Representation Learning for Zero-Shot Action Recognition (CVPR 2022 · similarity 0.89)
- Knowledge Integration Networks for Action Recognition (AAAI 2020 · similarity 0.88)
- Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs (CVPR 2018 · similarity 0.88)
- Two-Streams: Dark and Light Networks with Graph Convolution for Action Recognition from Dark Videos (Student Abstract) (AAAI 2023 · similarity 0.88)
- Prompt-guided Disentangled Representation for Action Recognition (NeurIPS 2025 · similarity 0.88)
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