Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

TL;DR

This paper introduces a novel fashion item detection task using deep convolutional neural networks combined with pose-dependent priors to improve accuracy in identifying clothing and accessories in images.

Contribution

It presents a new approach that integrates pose information with CNN-based object detection for fashion items, enhancing detection performance.

Findings

Pose-dependent priors improve detection accuracy.

The method outperforms baseline models on fashion item detection.

Contextual pose information significantly aids in locating fashion items.

Abstract

In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on. The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method pipeline which combines object proposal methods with a Deep Convolutional Neural Network. Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.