BraIN: A Bidirectional Generative Adversarial Networks for image captions

Although progress has been made in image captioning, machine-generated captions and human-generated captions are still quite distinct. Machine-generated captions perform well based on automated metrics. However, they lack naturalness, an essential characteristic of human language, because they maximize the likelihood of training samples. We propose a novel model to generate more human-like captions than has been accomplished with prior methods. Our model includes an attention mechanism, a bidirectional language generation model, and a conditional generative adversarial network. Specifically, the attention mechanism captures image details by segmenting important information into smaller pieces. The bidirectional language generation model produces human-like sentences by considering multiple perspectives. Simultaneously, the conditional generative adversarial network increases sentence quality by comparing a set of captions. To evaluate the performance of our model, we compare human preferences for BraIN-generated captions with baseline methods. We also compare results with actual human-generated captions using automated metrics. Results show our model is capable of producing more human-like captions than baseline methods.