  We present OSFormer, the first one-stage transformer framework for
camouflaged instance segmentation (CIS). OSFormer is based on two key designs.
First, we design a location-sensing transformer (LST) to obtain the location
label and instance-aware parameters by introducing the location-guided queries
and the blend-convolution feedforward network. Second, we develop a
coarse-to-fine fusion (CFF) to merge diverse context information from the LST
encoder and CNN backbone. Coupling these two components enables OSFormer to
efficiently blend local features and long-range context dependencies for
predicting camouflaged instances. Compared with two-stage frameworks, our
OSFormer reaches 41% AP and achieves good convergence efficiency without
requiring enormous training data, i.e., only 3,040 samples under 60 epochs.
Code link: https://github.com/PJLallen/OSFormer.

Computer Vision and Pattern Recognition