  Camouflaged objects adaptively fit their color and texture with the
environment, which makes them indistinguishable from the surroundings. Current
methods revealed that high-level semantic features can highlight the
differences between camouflaged objects and the backgrounds. Consequently, they
integrate high-level semantic features with low-level detailed features for
accurate camouflaged object detection (COD). Unlike previous designs for
multi-level feature fusion, we state that enhancing low-level features is more
impending for COD. In this paper, we propose an overlapped window cross-level
attention (OWinCA) to achieve the low-level feature enhancement guided by the
highest-level features. By sliding an aligned window pair on both the highest-
and low-level feature maps, the high-level semantics are explicitly integrated
into the low-level details via cross-level attention. Additionally, it employs
an overlapped window partition strategy to alleviate the incoherence among
windows, which prevents the loss of global information. These adoptions enable
the proposed OWinCA to enhance low-level features by promoting the separability
of camouflaged objects. The associated proposed OWinCANet fuses these enhanced
multi-level features by simple convolution operation to achieve the final COD.
Experiments conducted on three large-scale COD datasets demonstrate that our
OWinCANet significantly surpasses the current state-of-the-art COD methods.
