Human Parsing/Object Segmentation论文
CVPR 2020
- Part-aware Context Network for Human Parsing
- Hierarchical Human Parsing with Typed Part-Relation Reasoning
- Self-Learning with Rectification Strategy for Human Parsing
ICCV 2019
- Learning Compositional Neural Information Fusion for Human Parsing
- Multi-class Part Parsing with Joint Boundary-Semantic Awareness
CVPR 2019
- Graphonomy: Universal Human Parsing via Graph Transfer Learning
- Parsing R-CNN for Instance-Level Human Analysis
MM 2019
- BraidNet: Braiding Semantics and Details for Accurate Human Parsing
AAAI 2019
- Devil in the Details: Towards Accurate Single and Multiple Human Parsing
ICIP 2019
- Improving Human Parsing by Extracting Global Information Using the Non-Local Operation
- Classification Assisted Segmentation Network for Human Parsing
CVPR 2018
- Weakly and semi supervised human body part parsing via pose-guided knowledge transfer
ECCV 2018
- Instance-level Human Parsing via Part Grouping Network
- Macro-Micro Adversarial Network for Human Parsing
- Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation
AAAI 2018
- Progressive Cognitive Human Parsing
MM 2018
- Trusted Guidance Pyramid Network for Human Parsing
ICME 2018
- Finer-Net: Cascaded Human Parsing with Hierarchical Granularity
CVPR 2017
- Look Into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing
- Joint Multi-Person Pose Estimation and Semantic Part Segmentation
- Interpretable structure evolving lstm
ICCV 2017
- Embedding 3D Geometric Features for Rigid Object Part Segmentation
Axiv
- Learning Semantic Neural Tree for Human Parsing(2019)
- Self-Correction for Human Parsing(2019)
Before 2017
- Semantic Part Segmentation using Compositional Model combining Shape and Appearance(CVPR 2015)
主要做动物的部件分割。提出数据集Horse-Cow:从PASCAL VOC 2010中手动选择一些可完全观察的动物实例(所以遮挡很少),标注了四种部件类别(头,腿,尾巴,身体)。训练集294,测试集227.
- Parsing semantic parts of cars using graphical models and segment appearance consistency(BMVC 2014)
- Poseguided human parsing by an and/or graph using pose-context features(AAAI 2016)
- Joint object and part segmentation using deep learned potentials(ICCV 2015)
- Semantic object parsing with graph lstm(ECCV 2016)
原文地址:https://www.cnblogs.com/tofengz/p/13285856.html