# Holistic, Instance-Level Human Parsing

arXiv: https://arxiv.org/abs/1709.03612 · HTML: https://mlsanity.com/paper/1709.03612/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 19 of 19)

- [Semantic-SAM: Segment and Recognize Anything at Any Granularity](https://mlsanity.com/paper/2307.04767/) (2023)
- [Part-aware Panoptic Segmentation](https://mlsanity.com/paper/2106.06351/) (2021)
- [Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation](https://mlsanity.com/paper/2204.04655/) (2022)
- [Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing](https://mlsanity.com/paper/2103.04570/) (2021)
- [PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation](https://mlsanity.com/paper/2301.00954/) (2023)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition](https://mlsanity.com/paper/2204.04654/) (2022)
- [FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter](https://mlsanity.com/paper/2104.00073/) (2021)
- [Multi-task Fusion for Efficient Panoptic-Part Segmentation](https://mlsanity.com/paper/2212.07671/) (2022)
- [Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer](https://mlsanity.com/paper/2101.10620/) (2021)
- [Transfer of Representations to Video Label Propagation: Implementation Factors Matter](https://mlsanity.com/paper/2203.05553/) (2022)
- [Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations](https://mlsanity.com/paper/2406.10114/) (2024)
- [Describe me if you can! Characterized Instance-level Human Parsing](https://mlsanity.com/paper/2201.09594/) (2022)
- [Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis](https://mlsanity.com/paper/2305.19547/) (2023)
- [JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation](https://mlsanity.com/paper/2311.18618/) (2023)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [Deep Learning for Human Parsing: A Survey](https://mlsanity.com/paper/2301.12416/) (2023)
- [Dynamic Graph Message Passing Networks for Visual Recognition](https://mlsanity.com/paper/2209.09760/) (2022)
- [Graph-Boosted Attentive Network for Semantic Body Parsing](https://mlsanity.com/paper/2407.05924/) (2024)

## Abstract (arXiv)

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.