Mind the Gap: Improving Success Rate of Vision-and-Language Navigation
  by Revisiting Oracle Success Routes
Chongyang Zhao Yuankai Qi Qi Wu
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.