Learning paradigms involving varying levels of supervision have received a
lot of interest within the computer vision and machine learning communities.
The supervisory information is typically considered to come from a human
supervisor -- a "teacher" figure. In this paper, we consider an alternate
source of supervision -- a "peer" -- i.e. a different machine. We introduce
cooperative learning, where two agents trying to learn the same visual
concepts, but in potentially different environments using different sources of
data (sensors), communicate their current knowledge of these concepts to each
other. Given the distinct sources of data in both agents, the mode of
communication between the two agents is not obvious. We propose the use of
visual attributes -- semantic mid-level visual properties such as furry,
wooden, etc.-- as the mode of communication between the agents. Our experiments
in three domains -- objects, scenes, and animals -- demonstrate that our
proposed cooperative learning approach improves the performance of both agents
as compared to their performance if they were to learn in isolation. Our
approach is particularly applicable in scenarios where privacy, security and/or
bandwidth constraints restrict the amount and type of information the two
agents can exchange.

本研究提出了一种 “合作学习” 方法，通过不同机器之间的共享中间级视觉属性信息，改善计算机视觉和机器学习领域中不同层次下的学习表现，尤其适用于存在隐私，安全性和带宽限制等情形下。