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Models/CA-FCN

CA-FCN

Reported on 6 benchmarks across 3 tasks · 1 paper · 6 SOTA

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

Computer Vision4 results

Scene ParsingonSVT
Accuracy· 2018-09-18
86.4
best: 99.1 (CLIP4STR-H (DFN-5B))
SOTA
Scene Text Recognition from Two-Dimensional Perspective arXiv:1809.06508
Scene ParsingonICDAR2013
Accuracy· 2018-09-18
91.5
best: 99.42 (CLIP4STR-L*)
SOTA
Scene Text Recognition from Two-Dimensional Perspective arXiv:1809.06508
Scene Text RecognitiononSVT
Accuracy· 2018-09-18
86.4
best: 99.1 (CLIP4STR-H (DFN-5B))
SOTA
Scene Text Recognition from Two-Dimensional Perspective arXiv:1809.06508
Scene Text RecognitiononICDAR2013
Accuracy· 2018-09-18
91.5
best: 99.42 (CLIP4STR-L*)
SOTA
Scene Text Recognition from Two-Dimensional Perspective arXiv:1809.06508

Audio2 results

2D Semantic SegmentationonSVT
Accuracy· 2018-09-18
86.4
best: 99.1 (CLIP4STR-H (DFN-5B))
SOTA
Scene Text Recognition from Two-Dimensional Perspective arXiv:1809.06508
2D Semantic SegmentationonICDAR2013
Accuracy· 2018-09-18
91.5
best: 99.42 (CLIP4STR-L*)
SOTA
Scene Text Recognition from Two-Dimensional Perspective arXiv:1809.06508