Research map / Video / Video-language understanding
Reading the Videos: Temporal Labeling for Crowdsourced Time-Sync Videos Based on Semantic Embedding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace word-distribution features with T-DSSM semantic-vector topic clusters | highlight labeling framework · Bilibili bullet-screen comments | F1: 0.3687 -> 0.4764 | Helped |
| replace LDA topics with T-DSSM semantic-vector clusters | highlight labeling framework · Bilibili bullet-screen comments | F1: 0.3847 -> 0.4764 | Helped |
| replace word-based/LDA with T-DSSM | label discovery · Bilibili bullet-screen comments | Recall of labels: 0.4547 -> 0.5738 | Helped |
| increase pass rate alpha from 0.1 to 0.5 (ablation) | T-DSSM framework · Bilibili bullet-screen comments | Precision / Recall | Mixed |
Closest papers by meaning
- Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding (CVPR 2025 · similarity 0.86)
- From Play to Replay: Composed Video Retrieval for Temporally Fine-Grained Videos (NeurIPS 2025 · similarity 0.86)
- Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval (AAAI 2025 · similarity 0.85)
- Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos (ICLR 2025 · similarity 0.85)
- Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark (AAAI 2025 · similarity 0.85)
- LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding (NeurIPS 2024 · similarity 0.85)
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