Collaborative Camouflaged Object Detection: A Large-Scale Dataset and
  Benchmark
Cong Zhang Hongbo Bi Tian-Zhu Xiang Ranwan Wu Jinghui Tong Xiufang Wang
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.