KGDet: Keypoint-Guided Fashion Detection
Keywords:Applications, Object Detection & Categorization
AbstractLocating 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.
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
AAAI Technical Track on Computer Vision II