  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.
