  In order to learn object segmentation models in videos, conventional methods
require a large amount of pixel-wise ground truth annotations. However,
collecting such supervised data is time-consuming and labor-intensive. In this
paper, we exploit existing annotations in source images and transfer such
visual information to segment videos with unseen object categories. Without
using any annotations in the target video, we propose a method to jointly mine
useful segments and learn feature representations that better adapt to the
target frames. The entire process is decomposed into two tasks: 1) solving a
submodular function for selecting object-like segments, and 2) learning a CNN
model with a transferable module for adapting seen categories in the source
domain to the unseen target video. We present an iterative update scheme
between two tasks to self-learn the final solution for object segmentation.
Experimental results on numerous benchmark datasets show that the proposed
method performs favorably against the state-of-the-art algorithms.
