DialMAT: Dialogue-Enabled Transformer with Moment-Based Adversarial Training
This addresses the problem of improving interactive AI agents in embodied environments, representing a strong incremental advance in a specific domain.
The paper tackles the DialFRED task of embodied instruction following with an agent that can ask questions, proposing DialMAT with moment-based adversarial training and crossmodal feature extraction, achieving top performance in the DialFRED Challenge with superior success rates.
This paper focuses on the DialFRED task, which is the task of embodied instruction following in a setting where an agent can actively ask questions about the task. To address this task, we propose DialMAT. DialMAT introduces Moment-based Adversarial Training, which incorporates adversarial perturbations into the latent space of language, image, and action. Additionally, it introduces a crossmodal parallel feature extraction mechanism that applies foundation models to both language and image. We evaluated our model using a dataset constructed from the DialFRED dataset and demonstrated superior performance compared to the baseline method in terms of success rate and path weighted success rate. The model secured the top position in the DialFRED Challenge, which took place at the CVPR 2023 Embodied AI workshop.