Hierarchical Attention-Based Fusion for Image Caption With Multi-Grained Rewards
Chunlei Wu, Shaozu Yuan, Haiwen Cao, Yiwei Wei, Leiquan Wang
Abstract
Image caption based on reinforcement learning (RL) methods has achieved significant success recently. Most of these methods take CIDEr score as the reward of reinforcement learning algorithm to compute gradients, thus refining the image caption baseline model. However, CIDEr score is not the sole criterion to judge the quality of a generated caption. In this paper, a Hierarchical Attention Fusion (HAF) model is presented as a baseline for image caption based on RL, where multi-level feature maps of Resnet are integrated with hierarchical attention. Revaluation network (REN) is exploited for revaluating CIDEr score by assigning different weights for each word according to the importance of each word in a generating caption. The weighted reward can be regarded as word-level reward. Moreover, Scoring Network (SN) is implemented to score the generating sentence with its corresponding ground truth from a batch of captions. This reward can obtain benefits from additional unmatched ground truth, which acts as sentence-level reward. Experimental results on the COCO dataset show that the proposed methods have achieved competitive performance compared with the related image caption methods.