Human Parsing
56 papers with code • 1 benchmarks • 2 datasets
Human parsing is the task of segmenting a human image into different fine-grained semantic parts such as head, torso, arms and legs.
( Image credit: Multi-Human-Parsing (MHP) )
Benchmarks
These leaderboards are used to track progress in Human Parsing
| Trend | Dataset | Best Model | Paper | Code | Compare |
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PASCAL Context
MHP
Latest papers with no code
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks.
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i. e. various levels of label granularity) to train a powerful human parsing network.
Texture-Based Input Feature Selection for Action Recognition
To improve the model robustness, we propose a novel method to determine the task-irrelevant content in inputs which increases the domain discrepancy.
Deep Learning for Human Parsing: A Survey
Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classification, among many others.
Google Coral-based edge computing person reidentification using human parsing combined with analytical method
We also implement the analytical part of re-ID method on Coral CPU to ensure that it can perform a complete re-ID cycle.
RepParser: End-to-End Multiple Human Parsing with Representative Parts
Different from mainstream methods, RepParser solves the multiple human parsing in a new single-stage manner without resorting to person detection or post-grouping. To this end, RepParser decouples the parsing pipeline into instance-aware kernel generation and part-aware human parsing, which are responsible for instance separation and instance-specific part segmentation, respectively.
Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification
That is, models trained to achieve high accuracy on one dataset perform poorly on other ones and require re-training.
AIParsing: Anchor-free Instance-level Human Parsing
Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level.
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training
Recently, much progress has been made for self-supervised action recognition.
Describe me if you can! Characterized Instance-level Human Parsing
In addition, we propose HPTR, a new bottom-up multi-task method based on transformers as a fast and scalable baseline.
