Self-critical Sequence Training for Image Captioning

TL;DR

This paper introduces self-critical sequence training (SCST), a reinforcement learning method that directly optimizes image captioning models on evaluation metrics, leading to significant performance improvements on the MSCOCO dataset.

Contribution

The paper proposes SCST, a novel reinforcement learning approach that uses the model's own test-time inference output for reward normalization, simplifying training and improving results.

Findings

Achieved new state-of-the-art CIDEr score of 114.7 on MSCOCO.

Direct optimization of CIDEr improves caption quality.

SCST outperforms previous reinforcement learning methods in image captioning.

Abstract

Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized. Our systems are built using a new optimization approach that we call self-critical sequence training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather than estimating a "baseline" to normalize the rewards and reduce variance, utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences. Using this approach, estimating the reward signal (as actor-critic methods must do) and estimating normalization (as REINFORCE algorithms typically do) is avoided, while at the same time harmonizing the model with respect to its test-time inference procedure. Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective. Our results on the MSCOCO evaluation sever establish a new state-of-the-art on the task, improving the best result in terms of CIDEr from 104.9 to 114.7.