  Recently, the emergence of the large-scale vision-language model (VLM), such
as CLIP, has opened the way towards open-world object perception. Many works
have explored the utilization of pre-trained VLM for the challenging
open-vocabulary dense prediction task that requires perceiving diverse objects
with novel classes at inference time. Existing methods construct experiments
based on the public datasets of related tasks, which are not tailored for open
vocabulary and rarely involve imperceptible objects camouflaged in complex
scenes due to data collection bias and annotation costs. To fill in the gaps,
we introduce a new task, open-vocabulary camouflaged object segmentation
(OVCOS), and construct a large-scale complex scene dataset (\textbf{OVCamo})
containing 11,483 hand-selected images with fine annotations and corresponding
object classes. Further, we build a strong single-stage open-vocabulary
\underline{c}amouflaged \underline{o}bject \underline{s}egmentation
transform\underline{er} baseline \textbf{OVCoser} attached to the
parameter-fixed CLIP with iterative semantic guidance and structure
enhancement. By integrating the guidance of class semantic knowledge and the
supplement of visual structure cues from the edge and depth information, the
proposed method can efficiently capture camouflaged objects. Moreover, this
effective framework also surpasses previous state-of-the-arts of
open-vocabulary semantic image segmentation by a large margin on our OVCamo
dataset. With the proposed dataset and baseline, we hope that this new task
with more practical value can further expand the research on open-vocabulary
dense prediction tasks. Our code and data can be found in the
\href{https://github.com/lartpang/OVCamo}{link}.
