Scene Parsing
75 papers with code • 2 benchmarks • 4 datasets
Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description
Benchmarks
These leaderboards are used to track progress in Scene Parsing
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Libraries
Use these libraries to find Scene Parsing models and implementations
Cityscapes
Stanford Background
PGDP5K
UNDD
Subtasks
Most implemented papers
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model.
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
tensorflow/models
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NeurIPS 2018
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
Auto-Retoucher(ART) - A framework for Background Replacement and Image Editing
woshiyyya/Auto-Retoucher-pytorch
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Replacing the background and simultaneously adjusting foreground objects is a challenging task in image editing.
Toward Achieving Robust Low-Level and High-Level Scene Parsing
Furthermore, we introduce a “dense skip” architecture to retain a rich set of low-level information from the pre-trained CNN, which is essential to improve the low-level parsing performance.
Structured Knowledge Distillation for Dense Prediction
irfanICMLL/structure_knowledge_distillation
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CVPR 2019
Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem.
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
zju-vipa/KamalEngine
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CVPR 2019
In this paper, we investigate a novel deep-model reusing task.
Structured Knowledge Distillation for Semantic Segmentation
irfanICMLL/structure_knowledge_distillation
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CVPR 2019
We further propose to distill the structured knowledge from cumbersome networks into compact networks, which is motivated by the fact that semantic segmentation is a structured prediction problem.
Scene Parsing via Integrated Classification Model and Variance-Based Regularization
On the one hand, the integrated classification model contains multiple classifiers, not only the general classifier but also a refinement classifier to distinguish the confusing categories.
Adaptive Pyramid Context Network for Semantic Segmentation
Dylan-get/APCNet
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CVPR 2019
Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks.
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
kashyap7x/QGN
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Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions.
