- Jia Chen, Shizhe Chen, Qin Jin, Alexander Hauptmann, Po-Yao (Bernie) Huang, Junwei Liang, Vaibhav, Xiaojun Chang, Jiang Liu, Ting-yao Hu, Wenhe Liu, Wei Ke, Wayner Barrios, Haroon Idrees, Donghyun Yoo, Yaser Sheikh, R. Salakhutdinov, Kris Kitani, Dong Huang. 2018. Informedia @ TRECVID 2018: Ad-hoc Video Search, Video to Text Description, Activities in Extended video. Abstract: In this section of the notebook, we present our system in the TRECVID Video to Text description generation task. The optimization target is critical to train the encoder-decoder based video captioning models. However, there are two main limitations of the most widely used cross-entropy (CE) function as the training target, namely exposure bias and mismatched targets in training and testing. Therefore, we propose to utilize the reinforcement-learning algorithm with different rewards to improve the video captioning performance, which has achieved substantial gains over the CE-trained baselines.
