Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark

• URL: http://arxiv.org/abs/2310.04253v1
• Date: Fri, 6 Oct 2023 13:51:46 GMT
• Title: Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark
• Authors: Cong Zhang, Hongbo Bi, Tian-Zhu Xiang, Ranwan Wu, Jinghui Tong, Xiufang Wang
• Abstract summary: We study a new task called collaborative camouflaged object detection (CoCOD) CoCOD aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. We construct the first large-scale dataset, termed CoCOD8K, which consists of 8,528 high-quality and elaborately selected images.
• Score: 8.185431179739945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we provide a comprehensive study on a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. To this end, we meticulously construct the first large-scale dataset, termed CoCOD8K, which consists of 8,528 high-quality and elaborately selected images with object mask annotations, covering 5 superclasses and 70 subclasses. The dataset spans a wide range of natural and artificial camouflage scenes with diverse object appearances and backgrounds, making it a very challenging dataset for CoCOD. Besides, we propose the first baseline model for CoCOD, named bilateral-branch network (BBNet), which explores and aggregates co-camouflaged cues within a single image and between images within a group, respectively, for accurate camouflaged object detection in given images. This is implemented by an inter-image collaborative feature exploration (CFE) module, an intra-image object feature search (OFS) module, and a local-global refinement (LGR) module. We benchmark 18 state-of-the-art models, including 12 COD algorithms and 6 CoSOD algorithms, on the proposed CoCOD8K dataset under 5 widely used evaluation metrics. Extensive experiments demonstrate the effectiveness of the proposed method and the significantly superior performance compared to other competitors. We hope that our proposed dataset and model will boost growth in the COD community. The dataset, model, and results will be available at: https://github.com/zc199823/BBNet--CoCOD.

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