# FOAM: A Follower-aware Speaker Model For Vision-and-Language Navigation Source: [FOAM: A Follower-aware Speaker Model For Vision-and-Language Navigation]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:16:46.056Z Authors: Zi-Yi Dou, Nanyun Peng Published: 2022-06-09T06:11:07.000Z ## Abstract The speaker-follower models have proven to be effective in vision-and-language navigation, where a speaker model is used to synthesize new instructions to augment the training data for a follower navigation model. However, in many of the previous methods, the generated instructions are not directly trained to optimize the performance of the follower. In this paper, we present \\textsc{foam}, a \\textsc{Fo}llower-\\textsc{a}ware speaker \\textsc{M}odel that is constantly updated given the follower feedback, so that the generated instructions can be more suitable to the current learning state of the follower. Specifically, we optimize the speaker using a bi-level optimization framework and obtain its training signals by evaluating the follower on labeled data. Experimental results on the Room-to-Room and Room-across-Room datasets demonstrate that our methods can outperform strong baseline models across settings. Analyses also reveal that our generated instructions are of higher quality than the baselines. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Vision-and-Language Navigation on RxR (Room-Across-Room) unseen (val)](): Vision-and-Language Navigation; RxR (Room-Across-Room) unseen (val); SR (Success Rate): 42.8. ## Original sources and implementation [Original paper]() [PDF]()