Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use. In this work, we propose FEELVOS as a simple and fast method which does not rely on fine-tuning. In order to segment a video, for each frame FEELVOS uses a semantic pixel-wise embedding together with a global and a local matching mechanism to transfer information from the first frame and from the previous frame of the video to the current frame. In contrast to previous work, our embedding is only used as an internal guidance of a convolutional network. Our novel dynamic segmentation head allows us to train the network, including the embedding, end-to-end for the multiple object segmentation task with a cross entropy loss. We achieve a new state of the art in video object segmentation without fine-tuning with a J &F measure of 71.5% on the DAVIS 2017 validation set. We make our code and models available at https://github.com/tensorflow/ models/tree/master/research/feelvos. * Work done during an internship at Google Inc. † Now at Waymo LLC. Simple Fast End-to-end Strong PML [6] OSMN [40] FAVOS [7] VideoMatch [17] RGMP [37] FEELVOS (ours) PReMVOS [26] OnAVOS [35] 9473
Our method is inspired by FEELVOS REF , and utilizes the global and local matching maps to transfer information of the scribble-annotated and previous frame to the target frame.
FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science