  Concealed scene understanding (CSU) is a hot computer vision topic aiming to
perceive objects exhibiting camouflage. The current boom in terms of techniques
and applications warrants an up-to-date survey. This can help researchers to
better understand the global CSU field, including both current achievements and
remaining challenges. This paper makes four contributions: (1) For the first
time, we present a comprehensive survey of deep learning techniques aimed at
CSU, including a taxonomy, task-specific challenges, and ongoing developments.
(2) To allow for an authoritative quantification of the state-of-the-art, we
offer the largest and latest benchmark for concealed object segmentation (COS).
(3) To evaluate the generalizability of deep CSU in practical scenarios, we
collect the largest concealed defect segmentation dataset termed CDS2K with the
hard cases from diversified industrial scenarios, on which we construct a
comprehensive benchmark. (4) We discuss open problems and potential research
directions for CSU. Our code and datasets are available at
https://github.com/DengPingFan/CSU, which will be updated continuously to watch
and summarize the advancements in this rapidly evolving field.
