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Advances in Deep Concealed Scene Understanding (2304.11234v2)

Published 21 Apr 2023 in cs.CV

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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Summary

Advances in Deep Concealed Scene Understanding

This paper, titled "Advances in Deep Concealed Scene Understanding," offers an exhaustive examination of recent developments in the field, specifically focusing on concealed scene understanding (CSU). CSU is a challenging domain within computer vision aimed at detecting camouflaged objects and environments, which has garnered significant attention due to its numerous applications across fields such as safety, healthcare, agriculture, and content creation.

Overview of Contributions

The paper makes several noteworthy contributions to the CSU community:

  1. Comprehensive Survey: The authors present a detailed survey of CSU methodologies, underscoring the importance of deep learning frameworks. This survey encapsulates current techniques, task-specific challenges, and ongoing developments, providing a taxonomy that classifies the myriad approaches in the field.
  2. Benchmark for Concealed Object Segmentation: The authors introduce the largest and most recent benchmark for concealed object segmentation (COS), a pivotal task within CSU. This benchmark serves as a cornerstone for evaluating state-of-the-art techniques under consistent and reliable settings.
  3. Concealed Defect Segmentation Dataset: A novel contribution in this paper is the CDS2K dataset, which targets the concealed defect segmentation task by incorporating challenging samples from various industrial settings. This dataset is intended to facilitate the application of CSU techniques to real-world scenarios and serves as an essential benchmark for evaluating the robustness and generalizability of deep learning models in practical contexts.
  4. Discussion of Open Problems and Future Directions: The paper concludes with an exploration of open problems and potential avenues for future research, such as unsolved issues in CSU, the interaction between ongoing interdisciplinary efforts, and how benchmarks can drive further progress.

Key Findings and Analysis

The paper identifies several aspects where deep learning techniques have significantly contributed to CSU. A variety of network architectures, such as multi-stream frameworks and transformer-based models, demonstrate the ability to capture diverse features necessary for CSU tasks. Despite these advancements, typical deep learning techniques remain data-hungry and resource-intensive, highlighting the need for data-efficient strategies. Furthermore, the benchmark analyses reveal the challenges posed by complex CSU tasks that require more sophisticated interactions between various methods developed across different computer vision tasks.

Implications and Future Prospects

Practically, the implications of the research presented in this paper suggest the pressing need for larger, more diverse datasets, as well as better training strategies that address data scarcity. Theoretically, the insights derived from this comprehensive paper point towards potential synergies that could be harnessed through integrating domain-specific knowledge with cutting-edge machine learning advancements. Additionally, the promising impact of transformer models and advanced data augmentation strategies like AI-generated content (AIGC) suggests trends that future research should explore.

In sum, this paper serves not only as a retrospective summary of progress in the field of CSU but also as a forward-looking discourse on the direction that future research should take. It aims to inspire the CSU community to continue striving for more robust, generalizable, and efficient solutions to the rich and complex challenges presented by concealed scenes in diverse applications.

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  1. GitHub - DengPingFan/CSU: Concealed Scene Understanding, Visual Intelligence (VI), 2023 (69 stars)

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