Learning Individual Class Representation From Biased Multi-Label Data

Image recognition is a popular and important research field of computer vision. Recently with the development of deep learning technology, image recognition performance has been improved significantly. However with multi-label images, recognizing individual category is a challenging task. In order to address the problem, we propose a Feature Disintegrator (FD) that decomposes co-occurred instance features of multi-label into individual categories. In experimental evaluation, proposed method achieves the gains of mean average precision (mAP) up to 18.67% and 29.05% over baseline networks in ML-MNIST and ML-CIFAR, respectively. It shows 5.76% and 6.65% higher mAP than baseline on PASCAL VOC-2007 and MS-COCO data set respectively.