Research map / Video / Video-language understanding
Video Semantic Clustering with Sparse and Incomplete Tags
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add binary tags to visual features as clustering input | SpClust · TRECVID MED 2011 | NMI: 0.20 -> 0.74 | Helped |
| use tag detection scores instead of raw binary tags | SpClust · TRECVID MED 2011 | NMI: 0.73 -> 0.76 | Mixed |
| concatenate visual and tag features instead of using tags only | ClustRF · TRECVID MED 2011 | NMI: 0.81 -> 0.17 | Hurt |
| model visual and tag data separately in the forest rather than concatenated (CC-Forest vs HML-RF design) | clustering forest · TRECVID MED 2011 | NMI: 0.17 -> 0.90 | Helped |
| HML-RF vs flip MMC (best prior tag-based method) | video clustering · TRECVID MED 2011 | NMI: 0.88 -> 0.90 | Helped |
| HML-RF vs best baseline on NUS-WIDE | image clustering · NUS-WIDE | NMI: 0.73 -> 0.80 | Helped |
| HML-RF vs SpClust/MMC under 25% tag removal | HML-RF · TRECVID MED 2011 | relative NMI drop: 0.31 -> 0.19 | Helped |
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