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.
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 ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION 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.
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
#9 best model for Semantic Segmentation on Cityscapes val
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).