  Semantic segmentation has achieved huge progress via adopting deep Fully
Convolutional Networks (FCN). However, the performance of FCN based models
severely rely on the amounts of pixel-level annotations which are expensive and
time-consuming. To address this problem, it is a good choice to learn to
segment with weak supervision from bounding boxes. How to make full use of the
class-level and region-level supervisions from bounding boxes is the critical
challenge for the weakly supervised learning task. In this paper, we first
introduce a box-driven class-wise masking model (BCM) to remove irrelevant
regions of each class. Moreover, based on the pixel-level segment proposal
generated from the bounding box supervision, we could calculate the mean
filling rates of each class to serve as an important prior cue, then we propose
a filling rate guided adaptive loss (FR-Loss) to help the model ignore the
wrongly labeled pixels in proposals. Unlike previous methods directly training
models with the fixed individual segment proposals, our method can adjust the
model learning with global statistical information. Thus it can help reduce the
negative impacts from wrongly labeled proposals. We evaluate the proposed
method on the challenging PASCAL VOC 2012 benchmark and compare with other
methods. Extensive experimental results show that the proposed method is
effective and achieves the state-of-the-art results.
