Advances in Deep Concealed Scene Understanding

TL;DR

This paper provides a comprehensive survey of deep learning methods for concealed scene understanding, introduces new benchmarks for segmentation tasks, and discusses future research directions in this challenging field.

Contribution

It presents the first comprehensive survey, introduces the largest benchmark datasets, and evaluates deep CSU's generalizability across industrial scenarios.

Findings

Largest benchmark for concealed object segmentation (COS)

New dataset CDS2K for industrial defect segmentation

Discussion of open problems and future directions

Abstract

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.