AIGeN: An Adversarial Approach for Instruction Generation in VLN
This addresses the challenge of limited instruction data for VLN agents, offering a novel data augmentation method to enhance navigation performance in AI systems.
The authors tackled the problem of generating synthetic instructions for Vision-and-Language Navigation (VLN) by proposing AIGeN, a GAN-inspired architecture using GPT-2 and BERT, which improved an off-the-shelf VLN method's performance on datasets like REVERIE and R2R, achieving state-of-the-art results.
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.