To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates.
#3 best model for Semantic Segmentation on PASCAL VOC 2012 test
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.
SOTA for Semantic Segmentation on PASCAL VOC 2012 test (using extra training data)
ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.
#7 best model for Semantic Segmentation on PASCAL VOC 2012 val
There is large consent that successful training of deep networks requires many thousand annotated training samples.
CELL SEGMENTATION DATA AUGMENTATION ELECTRON MICROSCOPY IMAGE SEGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
#2 best model for Scene Segmentation on SUN-RGBD
A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner.
Pixel-wise image segmentation is demanding task in computer vision.
We focus on the challenging task of real-time semantic segmentation in this paper.
#3 best model for Real-Time Semantic Segmentation on CamVid
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.