In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We investigate the viability of two discrete GAN training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST.
Image captioning as a conditional GAN training with novel architectures, also study two discrete GAN training methods.  An improved GAN model for image captioning that proposes a context-aware LSTM captioner, introduces a stronger co-attentive discriminator with better performance, and uses SCST for GAN training.