CoRe: Color Regression for Multicolor Fashion Garments

TL;DR

This paper introduces CoRe, a deep learning architecture that improves multicolor fashion garment analysis by combining classification and regression to accurately predict RGB color values, addressing limitations of previous classification-only methods.

Contribution

The paper proposes a novel modular architecture that integrates color classification and regression with attention mechanisms for precise RGB prediction of multicolor garments.

Findings

Regression-based color detection outperforms classification-only methods.

The architecture effectively handles multicolor garments with accurate RGB predictions.

Each component of the architecture contributes to improved performance.

Abstract

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That's why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.