Fully convolutional neural networks (FCNNs) trained on a large number of
images with strong pixel-level annotations have become the new state of the art
for the semantic segmentation task. While there have been recent attempts to
learn FCNNs from image-level weak annotations, they need additional
constraints, such as the size of an object, to obtain reasonable performance.
To address this issue, we present motion-CNN (M-CNN), a novel FCNN framework
which incorporates motion cues and is learned from video-level weak
annotations. Our learning scheme to train the network uses motion segments as
soft constraints, thereby handling noisy motion information. When trained on
weakly-annotated videos, our method outperforms the state-of-the-art EM-Adapt
approach on the PASCAL VOC 2012 image segmentation benchmark. We also
demonstrate that the performance of M-CNN learned with 150 weak video
annotations is on par with state-of-the-art weakly-supervised methods trained
with thousands of images. Finally, M-CNN substantially outperforms recent
approaches in a related task of video co-localization on the YouTube-Objects
dataset.

利用视频级弱标注来学习包含运动线索的全卷积神经网络（motion-CNN）可有效解决图像分割任务中目标大小的约束问题，其表现优于 EM-Adapt，且只用 150 个视频标注便可达到其他弱监督方法上千张图像的性能，并在视频共定位任务上胜过最近的方法。