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.

翻译：本文对一种新任务——协同伪装物体检测（CoCOD）进行了全面研究，该任务旨在从一组相关图像中同步检测具有相同属性的伪装物体。为此，我们精心构建了首个大规模数据集CoCOD8K，包含8,528张高质量且精心筛选的图像，并配有物体掩膜标注，覆盖5个超类与70个子类。该数据集涵盖广泛的自然与人工伪装场景，具有多样化的物体外观和背景，使其成为极具挑战性的CoCOD数据集。此外，我们提出了首个CoCOD基线模型——双边分支网络（BBNet），该模型分别探索并聚合单张图像内部及组内图像间的协同伪装线索，以实现给定图像中的精确伪装物体检测。这通过三个模块实现：图像间协同特征探索模块（CFE）、图像内物体特征搜索模块（OFS）以及局部-全局细化模块（LGR）。我们在所提出的CoCOD8K数据集上，采用5种广泛使用的评估指标，对18种现有先进模型（包括12种COD算法和6种CoSOD算法）进行了基准测试。大量实验证明了所提方法的有效性，且其性能显著优于其他竞争对手。我们希望所提供的数据集和模型将推动COD社区的发展。数据集、模型及结果将发布于：https://github.com/zc199823/BBNet--CoCOD。