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Models/CDC

CDC

Reported on 21 benchmarks across 5 tasks · 2 papers · 21 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision15 results

VideoonTHUMOS’14
mAP IOU@0.3· 2017-03-04
40.1
best: 89.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
VideoonTHUMOS’14
mAP IOU@0.4· 2017-03-04
29.4
best: 86.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
VideoonTHUMOS’14
mAP IOU@0.5· 2017-03-04
23.3
best: 80.9 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
VideoonTHUMOS’14
mAP IOU@0.6· 2017-03-04
13.1
best: 71 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
VideoonTHUMOS’14
mAP IOU@0.7· 2017-03-04
7.9
best: 56.1 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Temporal Action LocalizationonTHUMOS’14
mAP IOU@0.3· 2017-03-04
40.1
best: 89.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Temporal Action LocalizationonTHUMOS’14
mAP IOU@0.4· 2017-03-04
29.4
best: 86.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Temporal Action LocalizationonTHUMOS’14
mAP IOU@0.5· 2017-03-04
23.3
best: 80.9 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Temporal Action LocalizationonTHUMOS’14
mAP IOU@0.6· 2017-03-04
13.1
best: 71 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Temporal Action LocalizationonTHUMOS’14
mAP IOU@0.7· 2017-03-04
7.9
best: 56.1 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Action LocalizationonTHUMOS’14
mAP IOU@0.3· 2017-03-04
40.1
best: 89.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Action LocalizationonTHUMOS’14
mAP IOU@0.4· 2017-03-04
29.4
best: 86.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Action LocalizationonTHUMOS’14
mAP IOU@0.5· 2017-03-04
23.3
best: 80.9 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Action LocalizationonTHUMOS’14
mAP IOU@0.6· 2017-03-04
13.1
best: 71 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Action LocalizationonTHUMOS’14
mAP IOU@0.7· 2017-03-04
7.9
best: 56.1 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515

Methodology5 results

Zero-Shot LearningonTHUMOS’14
mAP IOU@0.3· 2017-03-04
40.1
best: 89.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Zero-Shot LearningonTHUMOS’14
mAP IOU@0.4· 2017-03-04
29.4
best: 86.7 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Zero-Shot LearningonTHUMOS’14
mAP IOU@0.5· 2017-03-04
23.3
best: 80.9 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Zero-Shot LearningonTHUMOS’14
mAP IOU@0.6· 2017-03-04
13.1
best: 71 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515
Zero-Shot LearningonTHUMOS’14
mAP IOU@0.7· 2017-03-04
7.9
best: 56.1 (AdaTAD (VideoMAEv2-giant))
SOTA
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos arXiv:1703.01515

Audio1 result

10-shot image generationonBabies
FID· 2021-04-13
69.13
best: 48.83 (AdAM)
SOTA
Few-shot Image Generation via Cross-domain Correspondence arXiv:2104.06820