Joint Clothes Detection and Attribution Prediction via Anchor-free Framework with Decoupled Representation Transformer
Fankai Zeng, Mingbo Zhao, Zhao Zhang, Shanchuan Gao, Cheng Youn Lu · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Clothes attribution prediction is the key technology for users to automatically describe clothing characteristics. Most current methods are first to detect the multiple clothes, and then crop out the clothes and feed to a certain network for clothes attribution prediction. But this two-stage approach is time- and resource- consuming; on the other hand, one-stage approach can provide an effective and efficient solution by integrating clothes detection and attribution prediction into an end-to-end framework. But the one-stage approach tends to explore anchor-based detectors causing high sensitivity to the hyperparameters and high computational complexity from dense anchors. In addition, it may also confront with optimization contradiction problem in the training procedure, as the clothes detection and attribution prediction branches demand diverse optimization. In this work, to handle the above problems, we aim to develop an end-to-end anchor-free framework by involving an additional branch for joint clothes detection and attribution prediction. To handle the optimization contradiction in two branches, we encode the backbone feature map as pixel-level dense queries and decode them via deformable transformer as the output features that are fed into detection and prediction branches, respectively. In this way, the features of detection and prediction branches can be decoupled and the optimization contradiction can be naturally solved. To further enhance the prediction accuracy, we in the prediction branch also develop a special attention strategy and loss function to adaptively integrate the peer attribution relationships into feature learning as well as to avoid mutual suppression for hierarchical attributions. Extensive simulation results verify the effectiveness of the proposed work.