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\title{Supplementary Materials of $\boldsymbol{\alpha}$-Former: Local-Feature-Aware (L-FA) Transformer}

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    Computer Science Dept.\\
    Cranberry University\\
    Pittsburgh, Pennsylvania, USA
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\begin{document}
Thank you for reviewing our paper. We reply to the comments here.

\section{Model Design}
Yes, only using transformer can already achieve good performance in segmentation tasks. However, compared to CNN, the transformer typically relies on more computing resources and large datasets. However, the camouflaged instance segmentation task doesn't have a very large dataset. Hence, we do not use some classic transformer-based segmentation architecture. Instead, we try to combine CNN and transformer to decrease the request for dataset scale and computing resources. 

\section{edge fusion module}
Edge detection poses a significant challenge in current camouflaged instance segmentation methodologies. This challenge stems from the distinctive nature of camouflaged instance segmentation compared to conventional instance segmentation tasks. Identifying the target object is particularly arduous, especially along its boundaries, as it shares similar colors and textures with the background. Although our Binary Filter and backbone can already extract the information of the object edge, without a designed module to optimize the edge extraction, our model will still suffer from extracting some difficult edges. Due to this, we tried to add an edge fusion module and a loss to directly supervise the edge extraction. This additional module significantly enhances the architecture's capability to accurately delineate the edges of the target object.
\end{document}

