A Hierarchical Framwork with Improved Loss for Large-scale Multi-modal Video Identification

This paper introduces our solution for iQIYI Celebrity Video Identification Challenge. After analyzing the iQIYI-VID-2019 dataset, we find the distribution of the dataset is very unbalanced and there are many unlabeled samples in the validation set and the test set. For these challenge, we propose a hierarchical system which combines different models and fuses base classifiers. For the false detections and low-quality features in the dataset, we use a simple and reasonable strategy to fuse features. In order to detect videos more accurately, we choose an improved loss function for the learning of base classifiers. Experiment results show that our framework performs well and evaluation conducted by the organizers shows that our final result gets the ninth place online and mAP 88.08%.