Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence
  with Egocentric-Exocentric Vision
Authors: name: Yuping He, name: Yifei Huang, name: Guo Chen, name: Lidong Lu, name: Baoqi Pei, name: Jilan Xu, name: Tong Lu, name: Yoichi Sato

Perceiving the world from both egocentric (first-person) and exocentric
(third-person) perspectives is fundamental to human cognition, enabling rich
and complementary understanding of dynamic environments. In recent years,
allowing the machines to leverage the synergistic potential of these dual
perspectives has emerged as a compelling research direction in video
understanding. In this survey, we provide a comprehensive review of video
understanding from both exocentric and egocentric viewpoints. We begin by
highlighting the practical applications of integrating egocentric and
exocentric techniques, envisioning their potential collaboration across
domains. We then identify key research tasks to realize these applications.
Next, we systematically organize and review recent advancements into three main
research directions: (1) leveraging egocentric data to enhance exocentric
understanding, (2) utilizing exocentric data to improve egocentric analysis,
and (3) joint learning frameworks that unify both perspectives. For each
direction, we analyze a diverse set of tasks and relevant works. Additionally,
we discuss benchmark datasets that support research in both perspectives,
evaluating their scope, diversity, and applicability. Finally, we discuss
limitations in current works and propose promising future research directions.
By synthesizing insights from both perspectives, our goal is to inspire
advancements in video understanding and artificial intelligence, bringing
machines closer to perceiving the world in a human-like manner. A GitHub repo
of related works can be found at
https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision.