KGDet: Keypoint-Guided Fashion Detection

Authors

  • Shenhan Qian ShanghaiTech University Alibaba Group
  • Dongze Lian ShanghaiTech University
  • Binqiang Zhao Alibaba Group
  • Tong Liu Alibaba Group
  • Bohui Zhu Alibaba Group
  • Hai Li Ant Group
  • Shenghua Gao ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging

Keywords:

Applications, Object Detection & Categorization

Abstract

Locating and classifying clothes, usually referred to as clothing detection, is a fundamental task in fashion analysis. Motivated by the strong structural characteristics of clothes, we pursue a detection method enhanced by clothing keypoints, which is a compact and effective representation of structures. To incorporate the keypoint cues into clothing detection, we design a simple yet effective Keypoint-Guided clothing Detector, named KGDet. Such a detector can fully utilize information provided by keypoints with the following two aspects: i) integrating local features around keypoints to benefit both classification and regression; ii) generating accurate bounding boxes from keypoints. To effectively incorporate local features , two alternative modules are proposed. One is a multi-column keypoint-encoding-based feature aggregation module; the other is a keypoint-selection-based feature aggregation module. With either of the above modules as a bridge, a cascade strategy is introduced to refine detection performance progressively. Thanks to the keypoints, our KGDet obtains superior performance on the DeepFashion2 dataset and the FLD dataset with high efficiency.

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Published

2021-05-18

How to Cite

Qian, S., Lian, D., Zhao, B., Liu, T., Zhu, B., Li, H., & Gao, S. (2021). KGDet: Keypoint-Guided Fashion Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 2449-2457. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16346

Issue

Section

AAAI Technical Track on Computer Vision II