Thermal Image Segmentation
64 papers with code • 7 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Thermal Image Segmentation
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Libraries
Use these libraries to find Thermal Image Segmentation models and implementationsDatasets
MFNet
PST900
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
Rethinking Atrous Convolution for Semantic Image Segmentation
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.
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.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
microsoft/Swin-Transformer
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ICCV 2021
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.
Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.
Fully Convolutional Networks for Semantic Segmentation
pochih/fcn-pytorch
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CVPR 2015
Convolutional networks are powerful visual models that yield hierarchies of features.
Deep High-Resolution Representation Learning for Visual Recognition
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Fast-SCNN: Fast Semantic Segmentation Network
The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
NVlabs/SegFormer
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NeurIPS 2021
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders.
