We held the iQIYI Celebrity Video Identification Challenge in ACMMULTIMEDIA 2019. The purpose was to encourage the research on video-based person identification. We released the iQIYI-VID-2019 dataset, which contains 200K videos of 10K celebrities. In this paper, we introduce the organization of the challenge, the dataset, the evaluation process, and the results.

In this paper, we present a video-based emotion recognition system submitted to the EmotiW 2016 Challenge. The core module of this system is a hybrid network that combines recurrent neural network (RNN) and 3D convolutional networks (C3D) in a late-fusion fashion. RNN and C3D encode appearance and motion information in different ways. Specifically, RNN takes appearance features extracted by convolutional neural network (CNN) over individual video frames as input and encodes motion later, while C3D models appearance and motion of video simultaneously. Combined with an audio module, our system achieved a recognition accuracy of 59.02% without using any additional emotion-labeled video clips in training set, compared to 53.8% of the winner of EmotiW 2015. Extensive experiments show that combining RNN and C3D together can improve video-based emotion recognition noticeably.