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Semantic Segmentation

929 papers with code · Computer Vision

Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category.

Some example benchmarks for this task are Cityscapes, PASCAL VOC and ADE20K. Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU) and Pixel Accuracy metrics.

( Image credit: CSAILVision )

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Greatest papers with code

FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation

CVPR 2019 tensorflow/models

Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.

SEMANTIC SEGMENTATION VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

Searching for MobileNetV3

ICCV 2019 tensorflow/models

We achieve new state of the art results for mobile classification, detection and segmentation.

IMAGE CLASSIFICATION NEURAL ARCHITECTURE SEARCH OBJECT DETECTION SEMANTIC SEGMENTATION

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

ECCV 2018 tensorflow/models

The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.

IMAGE CLASSIFICATION LESION SEGMENTATION SEMANTIC SEGMENTATION

MobileNetV2: Inverted Residuals and Linear Bottlenecks

CVPR 2018 tensorflow/models

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.

IMAGE CLASSIFICATION OBJECT DETECTION SEMANTIC SEGMENTATION

Rethinking Atrous Convolution for Semantic Image Segmentation

17 Jun 2017tensorflow/models

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.

#3 best model for Semantic Segmentation on PASCAL VOC 2012 val (using extra training data)

SEMANTIC SEGMENTATION

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

2 Jun 2016tensorflow/models

ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.

SEMANTIC SEGMENTATION

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

BREAST TUMOUR CLASSIFICATION CROWD COUNTING DOMAIN GENERALIZATION IMAGE CLASSIFICATION OBJECT DETECTION SEMANTIC SEGMENTATION

ParseNet: Looking Wider to See Better

15 Jun 2015tensorflow/models

When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines.

SEMANTIC SEGMENTATION

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

22 Dec 2014tensorflow/models

This is due to the very invariance properties that make DCNNs good for high level tasks.

REAL-TIME SEMANTIC SEGMENTATION SCENE SEGMENTATION