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Models/U-TAE

U-TAE

Reported on 15 benchmarks across 5 tasks · 2 papers · 13 SOTA

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

Medical7 results

Image GenerationonSEN12MS-CR-TS
PSNR· 2021-07-16
27.05
best: 28.07 (SeqDMs)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Image GenerationonSEN12MS-CR-TS
RMSE· 2021-07-16
0.051
best: 0.045 (SeqDMs)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Image GenerationonSEN12MS-CR-TS
SSIM· 2021-07-16
0.849
best: 0.866 (UnCRtainTS σ)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Semantic SegmentationonPASTIS
Mean IoU (test)· 2021-07-16
63.1
best: 67.9 (Exchanger+Mask2Former)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Semantic SegmentationonPASTIS
Overall Accuracy· 2021-07-16
83.2
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Semantic SegmentationonGlobal Flood forecasting
F1 score· 2021-07-16
0.77
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Image GenerationonSEN12MS-CR-TS
SAM· 2021-07-16
11.649
best: 12.777 (SeqDMs)
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933

Computer Vision4 results

Image InpaintingonSEN12MS-CR-TS
PSNR· 2021-07-16
27.05
best: 28.07 (SeqDMs)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Image InpaintingonSEN12MS-CR-TS
RMSE· 2021-07-16
0.051
best: 0.045 (SeqDMs)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Image InpaintingonSEN12MS-CR-TS
SSIM· 2021-07-16
0.849
best: 0.866 (UnCRtainTS σ)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
Image InpaintingonSEN12MS-CR-TS
SAM· 2021-07-16
11.649
best: 12.777 (SeqDMs)
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933

Audio3 results

10-shot image generationonPASTIS
Mean IoU (test)· 2021-07-16
63.1
best: 67.9 (Exchanger+Mask2Former)
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
10-shot image generationonPASTIS
Overall Accuracy· 2021-07-16
83.2
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933
10-shot image generationonGlobal Flood forecasting
F1 score· 2021-07-16
0.77
SOTA
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks arXiv:2107.07933

Miscellaneous1 result

Crop Yield PredictiononSICKLE
MAPE (%)· 2023-11-29
49.63
SOTA
SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters arXiv:2312.00069