UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
This work addresses a domain-specific problem in computer vision for researchers and practitioners by improving optical flow estimation, though it is incremental as it builds on existing unsupervised methods with SAM integration.
The paper tackled the problem of unsupervised optical flow estimation being vulnerable to occlusions and motion boundaries by proposing UnSAMFlow, which integrates object-level information from the Segment Anything Model (SAM) and introduces a new smoothness loss based on homography, resulting in state-of-the-art performance on KITTI and Sintel datasets with sharp boundaries and efficient generalization.
Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore, we propose UnSAMFlow, an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further aggregate features on the object level. With all these adaptations, our method produces clear optical flow estimation with sharp boundaries around objects, which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.