In the last few years, the research interest in Vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance by exploiting synthetic training data. In this work, we propose AIGeN, a novel architecture inspired by Generative Adversarial Networks (GANs) that produces meaningful and well-formed synthetic instructions to improve navigation agents' performance. The model is composed of a Transformer decoder (GPT-2) and a Transformer encoder (BERT). During the training phase, the decoder generates sentences for a sequence of images describing the agent's path to a particular point while the encoder discriminates between real and fake instructions. Experimentally, we evaluate the quality of the generated instructions and perform extensive ablation studies. Additionally, we generate synthetic instructions for 217K trajectories using AIGeN on Habitat-Matterport 3D Dataset (HM3D) and show an improvement in the performance of an off-the-shelf VLN method. The validation analysis of our proposal is conducted on REVERIE and R2R and highlights the promising aspects of our proposal, achieving state-of-the-art performance.

通过使用生成对抗网络（GANs）的启示，提出了AIGeN，一种新颖的架构，用于生成有意义且形式良好的合成指令，从而改善导航代理的性能，该模型由Transformer解码器（GPT-2）和Transformer编码器（BERT）组成。经过实验证明，使用AIGeN在Habitat-Matterport 3D Dataset（HM3D）上对217K条路径生成合成指令可以改善现成的VLN方法的性能，其验证分析表明该提案的前景非常有希望，达到了最先进的性能水平。

AIGeN：视觉定位导航中的指令生成的对抗性方法