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Vision-and-Language Navigation (VLN) aims to navigate to the target locationby following a given instruction. Unlike existing methods focused on predictinga more accurate action at each step in navigation, in this paper, we make thefirst attempt to tackle a long-ignored problem in VLN: narrowing the gapbetween Success Rate (SR) and Oracle Success Rate (OSR). We observe aconsistently large gap (up to 9%) on four state-of-the-art VLN methods acrosstwo benchmark datasets: R2R and REVERIE. The high OSR indicates the robot agentpasses the target location, while the low SR suggests the agent actually failsto 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-shelfVLN models. Specially, we design a multi-module transformer-based model forlearning compact discriminative trajectory viewpoint representation, which isused to predict the confidence of being a target location as described in theinstruction. The proposed method is evaluated on three widely-adopted datasets:R2R, REVERIE and NDH, and shows promising results, demonstrating the potentialfor more future research.

Mind the Gap: Improving Success Rate of Vision-and-Language Navigation by Revisiting Oracle Success Routes