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"Global receptive convolution, GRC", "PASCAL VOC 2012: 79.42 mIoU", "Cityscapes: 80.53 mIoU", "ADE20K: 45.74 mIoU"
"Cityscapes: 83.1 mIoU using single scale and os=4", "Cityscapes: 83.2 mIoU using single scale and os=2", "Cityscapes: 83.8 mIoU using multi-scale and os=4", "Cityscapes: 84.0 mIoU using multi-scale and os=2", "COCOStuff164K: 49.0 mIoU using single scale and os=4", "COCOStuff164K: 49.4 mIoU using multi-scale and os=4", "Pascal Context: 63.8 mIoU using single scale and os=4", "Pascal Context: 64.9 mIoU using multi-scale and os=4"
"improving class predictions by learning to selectively exploit information from neighboring pixels"
"so-so"
"milestone", "promptable segmentation", "segment anything model, sam"
"Real-time"
"ADE20K validation set: 46.48 mIoU", "PASCAL Context validation set: 55.8 mIoU", "Cityscapes test set: 82.3 mIoU"
"ADE20K validation set: 46.0 mIoU (single-scale, ConvNet backbones)", "ADE20K validation set: 48.1 mIoU (multi-scale, ConvNet backbones)", "ADE20K validation set: 54.1 mIoU (single-scale, Transformer backbones)", "ADE20K validation set: 55.6.0 mIoU (multi-scale, Transformer backbones)",
"Cityscapes test set: 82.2 mIoU with ImageNet-1K pre-training", "Cityscapes test set: 83.1 mIoU with ImageNet-1K pre-training and Mapillary pre-training"
"ADE20K validation set: 50.77 mIoU", "PASCAL Context validation set: 55.6 mIoU", "Cityscapes validation set: 80.7 mIoU"
"on Cityscapes test set, Hierarchical Multi-Scale Attention gets mIoU 85.4%.", "on Mapillary validation set, it gets mIoU 61.1%."
"Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation"
"on Cityscapes test-fine set, Navie-Student gets mIoU 85.2%."
"the first work on FCN for semantic segmentation", "VOC 2012: 62.2 mAP"
"VOC 2012: 75.3 mAP"
"VOC 2012: 59.9 mAP"
om/content_ECCV_2018/html/Ruochen_Fan_Associating_Inter-Image_Salient_ECCV_2018_paper.html)
""
""
"PASCAL VOC 2012: 79.7 mIoU"
"Smooth Network for intra-class inconsistency using global pooling layer at the end of encoder, and Border Network for inter-class indistinction using focal loss as boundary loss", "deep supervision in both Smooth and Border Networks", "similarly in stacked hourglass networks, skip connection with tranformation including (Refinement Residual Block, proposed in RefineNet) RRB and (Channel Attention Block, proposed in SENet) CAB is used in this paper", "this paper combines many known tricks"
"introduction of panoptic segmentation"
"JSIS-Net"
"Liang-Chieh Chen's new work on NAS and Dense Image Prediction, including scene parsing, person-part segmentation and semantic image segmentation"
"Figure 5. Effectiveness of HDC (Hybrid Dilated Convolution) in eliminating the gridding effect."
"combination of watershed and neural networks"
"VOC 2012: 82.2 mAP"
"PASCAL VOC 2012: 85.4 mAP"
"PASCAL VOC 2012: 84.2 mAP"
"PASCAL VOC 2012: 85.7 mIoU"
"PASCAL-Context validation set: 46.52 mIoU", "ADE20K validation set: 38.37 mIoU"
"PASCAL VOC 2012: 59.9 mIoU"
"the first work on FCN for semantic segmentation", "PASCAL VOC 2012: 62.2 mAP"
"VOC 2012: 75.3 mAP"