  Deep convolutional neural networks (CNNs) have been immensely successful in
many high-level computer vision tasks given large labeled datasets. However,
for video semantic object segmentation, a domain where labels are scarce,
effectively exploiting the representation power of CNN with limited training
data remains a challenge. Simply borrowing the existing pretrained CNN image
recognition model for video segmentation task can severely hurt performance. We
propose a semi-supervised approach to adapting CNN image recognition model
trained from labeled image data to the target domain exploiting both semantic
evidence learned from CNN, and the intrinsic structures of video data. By
explicitly modeling and compensating for the domain shift from the source
domain to the target domain, this proposed approach underpins a robust semantic
object segmentation method against the changes in appearance, shape and
occlusion in natural videos. We present extensive experiments on challenging
datasets that demonstrate the superior performance of our approach compared
with the state-of-the-art methods.
