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Video Summarization via Semantic Attended Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add supervised keyframe sparsity loss (ablation) | SASUM · SumMe | F-score: 40.6±0.2% -> 45.3±0.1% | Helped |
| add supervised keyframe sparsity loss (ablation) | SASUM · TVSum | F-score: 53.9±0.3% -> 58.2±0.2% | Helped |
| add length sparsity constraint (delta=0.3) (ablation) | SASUM · SumMe | F-score: 40.6±0.2% -> 41.0±0.1% | Helped |
| add length sparsity constraint (delta=0.3) (ablation) | SASUM · TVSum | F-score: 53.9±0.3% -> 54.6±0.2% | Helped |
| vary length ratio delta away from 0.3 (ablation) | SASUMlength · SumMe and TVSum | F-score | Hurt |
| text-description-guided frame selection (SASUMsup) vs SUM-GANsup | video summarization · SumMe | F-score: 41.7% -> 45.3±0.1% | Helped |
| text-description-guided frame selection (SASUMsup) vs SUM-GANsup | video summarization · YouTube (no text annotations) | F-score: 62.5% -> 60.3±0.3% | Hurt |
Closest papers by meaning
- Video Summarization with Large Language Models (CVPR 2025 · similarity 0.89)
- Scaling Up Video Summarization Pretraining with Large Language Models (CVPR 2024 · similarity 0.88)
- V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning (AAAI 2025 · similarity 0.87)
- Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos (ICLR 2025 · similarity 0.86)
- CSTA: CNN-based Spatiotemporal Attention for Video Summarization (CVPR 2024 · similarity 0.85)
- SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM (NeurIPS 2024 · similarity 0.85)
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