Advances in Deep Concealed Scene Understanding

• URL: http://arxiv.org/abs/2304.11234v2
• Date: Sun, 2 Jul 2023 09:40:01 GMT
• Title: Advances in Deep Concealed Scene Understanding
• Authors: Deng-Ping Fan, Ge-Peng Ji, Peng Xu, Ming-Ming Cheng, Christos Sakaridis, Luc Van Gool
• Abstract summary: We present a comprehensive survey of deep learning techniques aimed at concealed scene understanding. We offer the largest and latest benchmark for concealed object segmentation. We discuss open problems and potential research directions for CSU.
• Score: 145.88351069150943
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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