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Human fashion understanding is one crucial computer vision task since it hascomprehensive information for real-world applications. This focus on jointhuman fashion segmentation and attribute recognition. Contrary to the previousworks that separately model each task as a multi-head prediction problem, ourinsight is to bridge these two tasks with one unified model via visiontransformer modeling to benefit each task. In particular, we introduce theobject query for segmentation and the attribute query for attribute prediction.Both queries and their corresponding features can be linked via maskprediction. Then we adopt a two-stream query learning framework to learn thedecoupled query representations.We design a novel Multi-Layer Rendering modulefor attribute stream to explore more fine-grained features. The decoder designshares the same spirit as DETR. Thus we name the proposed method\textit{Fahsionformer}. Extensive experiments on three human fashion datasetsillustrate the effectiveness of our approach. In particular, our method withthe same backbone achieve \textbf{relative 10\% improvements} than previousworks in case of \textit{a joint metric (AP$^{\text{mask}}_{\text{IoU+F}_1}$)for both segmentation and attribute recognition}. To the best of our knowledge,we are the first unified end-to-end vision transformer framework for humanfashion analysis. We hope this simple yet effective method can serve as a newflexible baseline for fashion analysis. Code is available athttps://github.com/xushilin1/FashionFormer.

Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition