Research map / Video / Video segmentation and tracking
From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Edge Boxes objectness scores as prior weights to LLR-SDS (ablation) | LLR-SDS · Topical Video Object Discovery (multiple) | average recall@8: 0.430 -> 0.547; 27.2% | Helped |
| Use objectness scores as prior weights (ablation) | LLR-SDS · Groundhog Day | average recall | No clear effect |
| Combine locality regularization with sparse dictionary selection | LLR-SDS · Groundhog Day | average recall | Helped |
| Select representatives using objectness scores alone | Objectness · Topical Video Object Discovery (multiple) | average recall@8: 0.173 | Hurt |
| Add locality prior to sparse dictionary selection to suppress outliers | LLR-SDS · Synthetic data with three clusters and uniform background noise | outlier suppression | Helped |
Closest papers by meaning
- Summary Transfer: Exemplar-Based Subset Selection for Video Summarization (CVPR 2016 · similarity 0.86)
- Collaborative Summarization of Topic-Related Videos (CVPR 2017 · similarity 0.84)
- Deep Supervised Summarization: Algorithm and Application to Learning Instructions (NeurIPS 2019 · similarity 0.84)
- Video Summarization Using Denoising Diffusion Probabilistic Model (AAAI 2025 · similarity 0.82)
- Watch Video, Catch Keyword: Context-aware Keyword Attention for Moment Retrieval and Highlight Detection (AAAI 2025 · similarity 0.80)
- Adaptive Keyframe Sampling for Long Video Understanding (CVPR 2025 · similarity 0.80)
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