# DETR-based Layered Clothing Segmentation and Fine-Grained Attribute Recognition

arXiv: https://arxiv.org/abs/2304.08107 · HTML: https://mlsanity.com/paper/2304.08107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://mlsanity.com/paper/2004.12276/) (2020)
- [SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization](https://mlsanity.com/paper/1912.05027/) (2019)
- [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)
- [Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition](https://mlsanity.com/paper/2204.04654/) (2022)

## Built on by (most cited first, 1 of 1)

- [Human Shape and Clothing Estimation](https://mlsanity.com/paper/2402.18032/) (2024)

## Abstract (arXiv)

Clothing segmentation and fine-grained attribute recognition are challenging tasks at the crossing of computer vision and fashion, which segment the entire ensemble clothing instances as well as recognize detailed attributes of the clothing products from any input human images. Many new models have been developed for the tasks in recent years, nevertheless the segmentation accuracy is less than satisfactory in case of layered clothing or fashion products in different scales. In this paper, a new DEtection TRansformer (DETR) based method is proposed to segment and recognize fine-grained attributes of ensemble clothing instances with high accuracy. In this model, we propose a multi-layered attention module by aggregating features of different scales, determining the various scale components of a single instance, and merging them together. We train our model on the Fashionpedia dataset and demonstrate our method surpasses SOTA models in tasks of layered clothing segmentation and fine-grained attribute recognition.