  An intuition on human segmentation is that when a human is moving in a video,
the video-context (e.g., appearance and motion clues) may potentially infer
reasonable mask information for the whole human body. Inspired by this, based
on popular deep convolutional neural networks (CNN), we explore a very-weakly
supervised learning framework for human segmentation task, where only an
imperfect human detector is available along with massive weakly-labeled YouTube
videos. In our solution, the video-context guided human mask inference and CNN
based segmentation network learning iterate to mutually enhance each other
until no further improvement gains. In the first step, each video is decomposed
into supervoxels by the unsupervised video segmentation. The superpixels within
the supervoxels are then classified as human or non-human by graph optimization
with unary energies from the imperfect human detection results and the
predicted confidence maps by the CNN trained in the previous iteration. In the
second step, the video-context derived human masks are used as direct labels to
train CNN. Extensive experiments on the challenging PASCAL VOC 2012 semantic
segmentation benchmark demonstrate that the proposed framework has already
achieved superior results than all previous weakly-supervised methods with
object class or bounding box annotations. In addition, by augmenting with the
annotated masks from PASCAL VOC 2012, our method reaches a new state-of-the-art
performance on the human segmentation task.
