  Semi-supervised video object segmentation is a task of segmenting the target
object in a video sequence given only a mask annotation in the first frame. The
limited information available makes it an extremely challenging task. Most
previous best-performing methods adopt matching-based transductive reasoning or
online inductive learning. Nevertheless, they are either less discriminative
for similar instances or insufficient in the utilization of spatio-temporal
information. In this work, we propose to integrate transductive and inductive
learning into a unified framework to exploit the complementarity between them
for accurate and robust video object segmentation. The proposed approach
consists of two functional branches. The transduction branch adopts a
lightweight transformer architecture to aggregate rich spatio-temporal cues
while the induction branch performs online inductive learning to obtain
discriminative target information. To bridge these two diverse branches, a
two-head label encoder is introduced to learn the suitable target prior for
each of them. The generated mask encodings are further forced to be
disentangled to better retain their complementarity. Extensive experiments on
several prevalent benchmarks show that, without the need of synthetic training
data, the proposed approach sets a series of new state-of-the-art records. Code
is available at https://github.com/maoyunyao/JOINT.
