Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task and far more complicated than naively fusing the semantic and instance segmentation results. Prediction fusion is therefore important to achieve accurate panoptic segmentation... (read more)
PDF AbstractTASK | DATASET | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK | BENCHMARK |
---|---|---|---|---|---|---|
Panoptic Segmentation | COCO test-dev | REFINE (ResNeXt-101-DCN) | PQ | 51.5 | # 1 | |
PQst | 39.2 | # 2 | ||||
PQth | 59.6 | # 1 | ||||
Panoptic Segmentation | COCO test-dev | REFINE (ResNet-101-DCN) | PQ | 49.6 | # 4 | |
PQst | 37.7 | # 6 | ||||
PQth | 57.5 | # 4 |
METHOD | TYPE | |
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🤖 No Methods Found | Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet |