Complete Instances Mining for Weakly Supervised Instance Segmentation
Zecheng Li Zening Zeng Yuqi Liang Jin-Gang Yu
Weakly supervised instance segmentation (WSIS) using only image-level labels
is a challenging task due to the difficulty of aligning coarse annotations with
the finer task. However, with the advancement of deep neural networks (DNNs),
WSIS has garnered significant attention. Following a proposal-based paradigm,
we encounter a redundant segmentation problem resulting from a single instance
being represented by multiple proposals. For example, we feed a picture of a
dog and proposals into the network and expect to output only one proposal
containing a dog, but the network outputs multiple proposals. To address this
problem, we propose a novel approach for WSIS that focuses on the online
refinement of complete instances through the use of MaskIoU heads to predict
the integrity scores of proposals and a Complete Instances Mining (CIM)
strategy to explicitly model the redundant segmentation problem and generate
refined pseudo labels. Our approach allows the network to become aware of
multiple instances and complete instances, and we further improve its
robustness through the incorporation of an Anti-noise strategy. Empirical
evaluations on the PASCAL VOC 2012 and MS COCO datasets demonstrate that our
method achieves state-of-the-art performance with a notable margin. Our
implementation will be made available at https://github.com/ZechengLi19/CIM.