注意差距：通过重新审视Oracle成功路径提高视觉-语言导航的成功率
Mind the Gap: Improving Success Rate of Vision-and-Language Navigation by Revisiting Oracle Success Routes
AI总结：
本文首次针对VLN中成功率与Oracle成功率差距过大的问题，提出基于多模块Transformer从轨迹中挖掘目标位置的方法，在R2R、REVERIE和NDH上验证有效。
摘要：
视觉-语言导航（VLN）旨在通过遵循给定指令导航到目标位置。与现有方法专注于在导航的每一步预测更准确的动作不同，本文首次尝试解决VLN中一个长期被忽视的问题：缩小成功率（SR）与Oracle成功率（OSR）之间的差距。我们在两个基准数据集R2R和REVERIE上观察到四种最先进的VLN方法存在持续较大的差距（最高达9%）。较高的OSR表明机器人代理经过了目标位置，而较低的SR则表明代理最终实际上未能在目标位置停下。我们不是直接预测动作，而是提出从现成VLN模型给出的轨迹中挖掘目标位置。特别地，我们设计了一个基于多模块Transformer的模型，用于学习紧凑且具有判别力的轨迹视点表示，该表示用于预测某个位置作为指令中所述目标位置的置信度。所提出的方法在三个广泛采用的数据集R2R、REVERIE和NDH上进行了评估，并显示出有前景的结果，展示了未来更多研究的潜力。
英文摘要：
Vision-and-Language Navigation (VLN) aims to navigate to the target location by following a given instruction. Unlike existing methods focused on predicting a more accurate action at each step in navigation, in this paper, we make the first attempt to tackle a long-ignored problem in VLN: narrowing the gap between Success Rate (SR) and Oracle Success Rate (OSR). We observe a consistently large gap (up to 9%) on four state-of-the-art VLN methods across two benchmark datasets: R2R and REVERIE. The high OSR indicates the robot agent passes the target location, while the low SR suggests the agent actually fails to stop at the target location at last. Instead of predicting actions directly, we propose to mine the target location from a trajectory given by off-the-shelf VLN models. Specially, we design a multi-module transformer-based model for learning compact discriminative trajectory viewpoint representation, which is used to predict the confidence of being a target location as described in the instruction. The proposed method is evaluated on three widely-adopted datasets: R2R, REVERIE and NDH, and shows promising results, demonstrating the potential for more future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
