Really awesome semantic segmentation

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Deep Learning really-awesome-semantic-segmentation
Overview

really-awesome-semantic-segmentation

A list of all papers on Semantic Segmentation and the datasets they use. This site is maintained by Holger Caesar. From March 15, 2018, it will not be updated anymore. To complement or correct it, please contact me at holger-at-it-caesar.com or visit it-caesar.com. Also checkout really-awesome-gan and our COCO-Stuff dataset.

Dataset importance

Dataset importance plot

Details

For details which paper uses which dataset, please open the Google Drive document.

Survey papers

Online demos

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Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF
Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

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Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Segmentation Transformer Implementation of Segmentation Transformer in PyTorch, a new model to achieve SOTA in semantic segmentation while using trans

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Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Exploring Cross-Image Pixel Contrast for Semantic Segmentation Exploring Cross-Image Pixel Contrast for Semantic Segmentation, Wenguan Wang, Tianfei Z

Tianfei Zhou 510 Jan 2, 2023
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals.
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals.

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals This repo contains the Pytorch implementation of our paper: Unsupervised Seman

Wouter Van Gansbeke 335 Dec 28, 2022
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

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Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021, official Pytorch implementatio

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Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

SASSnet Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020) Our code is origin from UA-MT You can fin

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A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains (IJCV submission)
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains (IJCV submission)

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[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

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Comments
Owner
Holger Caesar
Author of the COCO-Stuff and nuScenes datasets.
Holger Caesar
GitHub
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Learning Pixel-level Semantic Affinity with Image-level Supervision This code is deprecated. Please see https://github.com/jiwoon-ahn/irn instead. Int

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Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP Abstract: We introduce a method that allows to automatically se

Daniil Pakhomov 134 Dec 19, 2022
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

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PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

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This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

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This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

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This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti

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Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

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