Real-time semantic segmentation is the task of achieving computationally efficient semantic segmentation (while maintaining a base level of accuracy).
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This is due to the very invariance properties that make DCNNs good for high level tasks.
SOTA for Scene Segmentation on SUN-RGBD
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
State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification.
#4 best model for Semantic Segmentation on CamVid
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses.
#8 best model for Real-Time Semantic Segmentation on Cityscapes
It is shown that skip architecture in the decoding method provides the best compromise for the goal of real-time performance, while it provides adequate accuracy by utilizing higher resolution feature maps for a more accurate segmentation.