Real-Time Semantic Segmentation
77 papers with code • 8 benchmarks • 12 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 )
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Most implemented papers
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications.
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features.
HarDNet: A Low Memory Traffic Network
We propose a Harmonic Densely Connected Network to achieve high efficiency in terms of both low MACs and memory traffic.
Fast-SCNN: Fast Semantic Segmentation Network
The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
SOLOv2: Dynamic and Fast Instance Segmentation
Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Semantic segmentation requires both rich spatial information and sizeable receptive field.
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
We focus on the challenging task of real-time semantic segmentation in this paper.
Lite-HRNet: A Lightweight High-Resolution Network
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.