A Fine-Grained Attribute Pre-Labeling Method Based on Label Dependency and Feature Similarity Dynamics

Abstract

In this paper, we proposed a fine-grained attribute pre-labeling method based on the multi-label recovery techniques. Given a fine-grained image dataset with overlooked attributes in its annotation vectors, our method can predict those missing attribute labels by learning the between-label dependency based on the estimated similarity between known attributes and the similarity of extracted deep image features. Furthermore, to prevent the learnable label dependency matrix from converging to a trivial solution, we designed a trace-loss to penalize the self-dependency of attributes. Comprehensive experiments on the CUB-200-2011 dataset show that, given a training set with 40% of attribute labels randomly dropped: i) our approach achieves a pre-labeling performance with an mAP value of 30.7 on a blind testing set, and ii) the missing attributes in the training set can be corrected with an accuracy of 89%. Our method can effectively and robustly perform the fine-grained pre-labeling task.