SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention

Muhammad Nawfal Meeran, Gokul Adethya T, Bhanu Pratyush Mantha; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1857-1866

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