Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation
CVPR 2024(2024)
Abstract
As a new embodied vision task, Instance ImageGoal Navigation (IIN) aims to
navigate to a specified object depicted by a goal image in an unexplored
environment.
The main challenge of this task lies in identifying the target object from
different viewpoints while rejecting similar distractors.
Existing ImageGoal Navigation methods usually adopt the simple
Exploration-Exploitation framework and ignore the identification of specific
instance during navigation.
In this work, we propose to imitate the human behaviour of “getting closer
to confirm" when distinguishing objects from a distance.
Specifically, we design a new modular navigation framework named
Instance-aware Exploration-Verification-Exploitation (IEVE) for instance-level
image goal navigation.
Our method allows for active switching among the exploration, verification,
and exploitation actions, thereby facilitating the agent in making reasonable
decisions under different situations.
On the challenging HabitatMatterport 3D semantic (HM3D-SEM) dataset, our
method surpasses previous state-of-the-art work, with a classical segmentation
model (0.684 vs. 0.561 success) or a robust model (0.702 vs. 0.561 success).
Our code will be made publicly available at https://github.com/XiaohanLei/IEVE.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined