  We present the first systematic study on concealed object detection (COD),
which aims to identify objects that are "perfectly" embedded in their
background. The high intrinsic similarities between the concealed objects and
their background make COD far more challenging than traditional object
detection/segmentation. To better understand this task, we collect a
large-scale dataset, called COD10K, which consists of 10,000 images covering
concealed objects in diverse real-world scenarios from 78 object categories.
Further, we provide rich annotations including object categories, object
boundaries, challenging attributes, object-level labels, and instance-level
annotations. Our COD10K is the largest COD dataset to date, with the richest
annotations, which enables comprehensive concealed object understanding and can
even be used to help progress several other vision tasks, such as detection,
segmentation, classification, etc. Motivated by how animals hunt in the wild,
we also design a simple but strong baseline for COD, termed the Search
Identification Network (SINet). Without any bells and whistles, SINet
outperforms 12 cutting-edge baselines on all datasets tested, making them
robust, general architectures that could serve as catalysts for future research
in COD. Finally, we provide some interesting findings and highlight several
potential applications and future directions. To spark research in this new
field, our code, dataset, and online demo are available on our project page:
http://mmcheng.net/cod.
