Mix Attention Based Convolutional Neural Network for Clothing Brand Logo Recognition and Classification - Liu, Kuan-Hsien; Chen, Guan-Hong; Liu, Tsung-Jung (Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, 2021)
Over the past years, fashion clothing related research problems such as clothing style classification, clothing attribute recognition, clothing recommendation, and clothing retrieval, have received great attention in the society of computer vision, pattern recognition and image processing. Recently, approaches based on deep learning have been proposed to tackle with aforementioned problems. However, no research work focusing on clothing brand identification/recognition is investigated. To deal with clothing brand identification problem, we construct a new very large-scale clothing dataset containing brand information and the brand logo bounding box information if it has one. Totally, we collected 416 fashion clothing brands in our newly built dataset. In this work, we propose a fashion Clothing Brand Recognition Network (CBR-Net), which contains two parts. First part is the identification network for the clothing brand logo detection and recognition, and the second part is an attention module based network for classifying clothing brands without logos. In the experiments, we demonstrated that our newly proposed CBR-Net can attain much better performance than other state-of-the-art methods.