  The area of Video Camouflaged Object Detection (VCOD) presents unique
challenges in the field of computer vision due to texture similarities between
target objects and their surroundings, as well as irregular motion patterns
caused by both objects and camera movement. In this paper, we introduce
TokenMotion (TMNet), which employs a transformer-based model to enhance VCOD by
extracting motion-guided features using a learnable token selection. Evaluated
on the challenging MoCA-Mask dataset, TMNet achieves state-of-the-art
performance in VCOD. It outperforms the existing state-of-the-art method by a
12.8% improvement in weighted F-measure, an 8.4% enhancement in S-measure, and
a 10.7% boost in mean IoU. The results demonstrate the benefits of utilizing
motion-guided features via learnable token selection within a transformer-based
framework to tackle the intricate task of VCOD.
