# Describe me if you can! Characterized Instance-level Human Parsing

arXiv: https://arxiv.org/abs/2201.09594 · HTML: https://mlsanity.com/paper/2201.09594/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 21 of 21)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Simultaneous Detection and Segmentation](https://mlsanity.com/paper/1407.1808/) (2014)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017)
- [Graphonomy: Universal Human Parsing via Graph Transfer Learning](https://mlsanity.com/paper/1904.04536/) (2019)
- [Devil in the Details: Towards Accurate Single and Multiple Human Parsing](https://mlsanity.com/paper/1809.05996/) (2018)
- [Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing](https://mlsanity.com/paper/1804.03287/) (2018)
- [Parsing R-CNN for Instance-Level Human Analysis](https://mlsanity.com/paper/1811.12596/) (2018)
- [Deep Human Parsing with Active Template Regression](https://mlsanity.com/paper/1503.02391/) (2015)
- [Joint Multi-Person Pose Estimation and Semantic Part Segmentation](https://mlsanity.com/paper/1708.03383/) (2017)
- [Renovating Parsing R-CNN for Accurate Multiple Human Parsing](https://mlsanity.com/paper/2009.09447/) (2020)
- [ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations](https://mlsanity.com/paper/1807.01394/) (2018)
- [Fashion Meets Computer Vision: A Survey](https://mlsanity.com/paper/2003.13988/) (2020)
- [Learning Semantic Neural Tree for Human Parsing](https://mlsanity.com/paper/1912.09622/) (2019)
- [Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing](https://mlsanity.com/paper/1911.12053/) (2019)
- [PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation](https://mlsanity.com/paper/2101.02471/) (2021)
- [Clothing Co-Parsing by Joint Image Segmentation and Labeling](https://mlsanity.com/paper/1502.00739/) (2015)
- [Holistic, Instance-Level Human Parsing](https://mlsanity.com/paper/1709.03612/) (2017)

## Built on by (most cited first, 3 of 3)

- [Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks](https://mlsanity.com/paper/2303.17602/) (2023)
- [MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video](https://mlsanity.com/paper/2303.10275/) (2023)
- [Parsing Objects at a Finer Granularity: A Survey](https://mlsanity.com/paper/2212.13693/) (2022)

## Abstract (arXiv)

Several computer vision applications such as person search or online fashion rely on human description. The use of instance-level human parsing (HP) is therefore relevant since it localizes semantic attributes and body parts within a person. But how to characterize these attributes? To our knowledge, only some single-HP datasets describe attributes with some color, size and/or pattern characteristics. There is a lack of dataset for multi-HP in the wild with such characteristics. In this article, we propose the dataset CCIHP based on the multi-HP dataset CIHP, with 20 new labels covering these 3 kinds of characteristics. In addition, we propose HPTR, a new bottom-up multi-task method based on transformers as a fast and scalable baseline. It is the fastest method of multi-HP state of the art while having precision comparable to the most precise bottom-up method. We hope this will encourage research for fast and accurate methods of precise human descriptions.