  Affordance detection refers to identifying the potential action possibilities
of objects in an image, which is a crucial ability for robot perception and
manipulation. To empower robots with this ability in unseen scenarios, we first
study the challenging one-shot affordance detection problem in this paper,
i.e., given a support image that depicts the action purpose, all objects in a
scene with the common affordance should be detected. To this end, we devise a
One-Shot Affordance Detection Network (OSAD-Net) that firstly estimates the
human action purpose and then transfers it to help detect the common affordance
from all candidate images. Through collaboration learning, OSAD-Net can capture
the common characteristics between objects having the same underlying
affordance and learn a good adaptation capability for perceiving unseen
affordances. Besides, we build a large-scale Purpose-driven Affordance Dataset
v2 (PADv2) by collecting and labeling 30k images from 39 affordance and 103
object categories. With complex scenes and rich annotations, our PADv2 dataset
can be used as a test bed to benchmark affordance detection methods and may
also facilitate downstream vision tasks, such as scene understanding, action
recognition, and robot manipulation. Specifically, we conducted comprehensive
experiments on PADv2 dataset by including 11 advanced models from several
related research fields. Experimental results demonstrate the superiority of
our model over previous representative ones in terms of both objective metrics
and visual quality. The benchmark suite is available at
https://github.com/lhc1224/OSAD Net.
