Camouflaged object detection intends to discover the concealed objects hidden
in the surroundings. Existing methods follow the bio-inspired framework, which
first locates the object and second refines the boundary. We argue that the
discovery of camouflaged objects depends on the recurrent search for the object
and the boundary. The recurrent processing makes the human tired and helpless,
but it is just the advantage of the transformer with global search ability.
Therefore, a dual-task interactive transformer is proposed to detect both
accurate position of the camouflaged object and its detailed boundary. The
boundary feature is considered as Query to improve the camouflaged object
detection, and meanwhile the object feature is considered as Query to improve
the boundary detection. The camouflaged object detection and the boundary
detection are fully interacted by multi-head self-attention. Besides, to obtain
the initial object feature and boundary feature, transformer-based backbones
are adopted to extract the foreground and background. The foreground is just
object, while foreground minus background is considered as boundary. Here, the
boundary feature can be obtained from blurry boundary region of the foreground
and background. Supervised by the object, the background and the boundary
ground truth, the proposed model achieves state-of-the-art performance in
public datasets. this https URL

本文提出了一种双任务互动转换器来检测伪装对象，该转换器采用了多头自我关注并考虑了边界特征，同时利用变形器进行特征提取，从而取得了当前公共数据集中的最新成果。