computer-vision-9
Real-Time Semantic Segmentation
3260 papers • 126 benchmarks • 313 datasets
Semantic Segmentation is a computer vision task that involves assigning a semantic label to each pixel in an image. In Real-Time Semantic Segmentation, the goal is to perform this labeling quickly and accurately in real-time, allowing for the segmentation results to be used for tasks such as object recognition, scene understanding, and autonomous navigation. ( Image credit: TorchSeg )
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in real-time-semantic-segmentation-9
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Dataset
Best Model
Actions
Cityscapes test
Cityscapes test
CamVid
CamVid
NYU Depth v2
NYU Depth v2
Libraries
iUse these libraries to find real-time-semantic-segmentation-9 models and implementations
16 papers 2,915
Datasets
Subtasks
No subtasks available.
Most implemented papers
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Alex Kendall, Vijay Badrinarayanan, R. Cipolla•Sun Nov 01 2015
Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.
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