Actor-Critic Sequence Generation for Relative Difference Captioning
Zhengcong Fei · 2020
This paper investigates a new task named relative difference caption which aims to generate a sentence to tell the difference between the given image pair. Difference description is a crucial task for developing intelligent machines that can understand and handle changeable visual scenes and applications. Towards that end, we propose a reinforcement learning-based model, which utilizes a policy network and a value network in a decision procedure to collaboratively produce a difference caption. Specifically, the policy network works as an actor to estimate the probability of next word based on the current state and the value network serves as a critic to predict all possible extension values according to current action and state. To encourage generating correct and meaningful descriptions, we leverage a visual-linguistic similarity-based reward function as feedback. Empirical results on the recently released dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants.