Real-Time Semantic Segmentation
86 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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Latest papers with no code
MCFNet: Multi-scale Covariance Feature Fusion Network for Real-time Semantic Segmentation
The low-level spatial detail information and high-level semantic abstract information are both essential to the semantic segmentation task.
P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation
In particular, our P2AT variants achieve state-of-art results on the Camvid dataset 80. 5%, 81. 0%, 81. 1% for P2AT-S, P2ATM, and P2AT-L, respectively.
Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing
With the success of efficient deep learning methods (i. e., efficient deep neural networks) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis.
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids.
Cross-CBAM: A Lightweight network for Scene Segmentation
And we propose a Cross Convolutional Block Attention Module(CCBAM), in which a cross-multiply operation is employed in the CCBAM module to make high-level semantic information guide low-level detail information.
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search
For local information exchange, a graph convolutional network (GCN) guided module is seamlessly integrated as a communication deliver between cells.
Efficient Semantic Segmentation on Edge Devices
Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class.
DWRSeg: Rethinking Efficient Acquisition of Multi-scale Contextual Information for Real-time Semantic Segmentation
In this method, the multi-rate depth-wise dilated convolutions take a simpler role in feature extraction: performing simple semantic-based morphological filtering with one desired receptive field in the second step based on each concise feature map of region form provided by the first step, to improve their efficiency.
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation
The main challenges of applying NAS to semantic segmentation arise from two aspects: (i) high-resolution images to be processed; (ii) additional requirement of real-time inference speed (i. e., real-time semantic segmentation) for applications such as autonomous driving.
Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation
In this paper, we intend to search an optimal network structure that can run in real-time for this problem.