Implicit Motion

Implicit motion modeling focuses on representing and utilizing motion information in videos without explicitly estimating optical flow or relying on traditional motion representations. Current research emphasizes developing neural network architectures that implicitly capture motion from temporal sequences, often leveraging techniques like Taylor expansions to highlight dominant motion patterns or employing dense correlation volumes to unify motion estimation and object segmentation. This approach improves performance in various applications, including video frame interpolation, action recognition, unsupervised video object segmentation, and camouflaged object detection, by offering robustness to noise and inaccuracies inherent in explicit motion estimation.