Object Discovery in Videos as Foreground Motion Clustering
This addresses object discovery in videos for computer vision applications, representing an incremental improvement.
The paper tackles the problem of dense segmentation masks for object discovery in videos by formulating it as foreground motion clustering, achieving state-of-the-art performance on motion segmentation datasets.
We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects. We introduce a novel pixel-trajectory recurrent neural network that learns feature embeddings of foreground pixel trajectories linked across time. By clustering the pixel trajectories using the learned feature embeddings, our method establishes correspondences between foreground object masks across video frames. To demonstrate the effectiveness of our framework for object discovery, we conduct experiments on commonly used datasets for motion segmentation, where we achieve state-of-the-art performance.