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In this paper, we provide a comprehensive study on a new task calledcollaborative camouflaged object detection (CoCOD), which aims tosimultaneously detect camouflaged objects with the same properties from a groupof relevant images. To this end, we meticulously construct the firstlarge-scale dataset, termed CoCOD8K, which consists of 8,528 high-quality andelaborately selected images with object mask annotations, covering 5superclasses and 70 subclasses. The dataset spans a wide range of natural andartificial camouflage scenes with diverse object appearances and backgrounds,making it a very challenging dataset for CoCOD. Besides, we propose the firstbaseline model for CoCOD, named bilateral-branch network (BBNet), whichexplores and aggregates co-camouflaged cues within a single image and betweenimages within a group, respectively, for accurate camouflaged object detectionin given images. This is implemented by an inter-image collaborative featureexploration (CFE) module, an intra-image object feature search (OFS) module,and a local-global refinement (LGR) module. We benchmark 18 state-of-the-artmodels, including 12 COD algorithms and 6 CoSOD algorithms, on the proposedCoCOD8K dataset under 5 widely used evaluation metrics. Extensive experimentsdemonstrate the effectiveness of the proposed method and the significantlysuperior performance compared to other competitors. We hope that our proposeddataset and model will boost growth in the COD community. The dataset, model,and results will be available at: https://github.com/zc199823/BBNet--CoCOD.

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