  In the domain of large foundation models, the Segment Anything Model (SAM)
has gained notable recognition for its exceptional performance in image
segmentation. However, tackling the video camouflage object detection (VCOD)
task presents a unique challenge. Camouflaged objects typically blend into the
background, making them difficult to distinguish in still images. Additionally,
ensuring temporal consistency in this context is a challenging problem. As a
result, SAM encounters limitations and falls short when applied to the VCOD
task. To overcome these challenges, we propose a new method called the SAM
Propagation Module (SAM-PM). Our propagation module enforces temporal
consistency within SAM by employing spatio-temporal cross-attention mechanisms.
Moreover, we exclusively train the propagation module while keeping the SAM
network weights frozen, allowing us to integrate task-specific insights with
the vast knowledge accumulated by the large model. Our method effectively
incorporates temporal consistency and domain-specific expertise into the
segmentation network with an addition of less than 1% of SAM's parameters.
Extensive experimentation reveals a substantial performance improvement in the
VCOD benchmark when compared to the most recent state-of-the-art techniques.
Code and pre-trained weights are open-sourced at
https://github.com/SpiderNitt/SAM-PM
