Most recent work in goal oriented visual navigation resorts to large-scale
machine learning in simulated environments. The main challenge lies in learning
compact representations generalizable to unseen environments and in learning
high-capacity perception modules capable of reasoning on high-dimensional
input. The latter is particularly difficult when the goal is not given as a
category ("ObjectNav") but as an exemplar image ("ImageNav"), as the perception
module needs to learn a comparison strategy requiring to solve an underlying
visual correspondence problem. This has been shown to be difficult from reward
alone or with standard auxiliary tasks. We address this problem through a
sequence of two pretext tasks, which serve as a prior for what we argue is one
of the main bottleneck in perception, extremely wide-baseline relative pose
estimation and visibility prediction in complex scenes. The first pretext task,
cross-view completion is a proxy for the underlying visual correspondence
problem, while the second task addresses goal detection and finding directly.
We propose a new dual encoder with a large-capacity binocular ViT model and
show that correspondence solutions naturally emerge from the training signals.
Experiments show significant improvements and SOTA performance on the two
benchmarks, ImageNav and the Instance-ImageNav variant, where camera intrinsics
and height differ between observation and goal.

通过使用预设任务和双编码器模型，本研究针对视觉导航中的视觉对应问题和目标检测进行了改进，取得了显著进展并在两个基准测试中达到了最佳性能。

通过对应关系实现端到端（实例）- 图像目标导航的新发现现象

End-to-End (Instance)-Image Goal Navigation through Correspondence as an  Emergent Phenomenon

Compact representations of objects is a common concept in computer science.
Automated planning can be viewed as a case of this concept: a planning instance
is a compact implicit representation of a graph and the problem is to find a
path (a plan) in this graph. While the graphs themselves are represented
compactly as planning instances, the paths are usually represented explicitly
as sequences of actions. Some cases are known where the plans always have
compact representations, for example, using macros. We show that these results
do not extend to the general case, by proving a number of bounds for compact
representations of plans under various criteria, like efficient sequential or
random access of actions. In addition to this, we show that our results have
consequences for what can be gained from reformulating planning into some other
problem. As a contrast to this we also prove a number of positive results,
demonstrating restricted cases where plans do have useful compact
representations, as well as proving that macro plans have favourable access
properties. Our results are finally discussed in relation to other relevant
contexts.

通过证明紧凑表示计划在不同标准下的边界，我们表明这些结果不能推广到普遍情况，同时证明一些正面结果，演示计划在某些情况下具有有用的紧凑表示，宏计划具有良好的访问属性。