  Weakly-supervised segmentation (WSS) methods, reliant on image-level labels
indicating object presence, lack explicit correspondence between labels and
regions of interest (ROIs), posing a significant challenge. Despite this, WSS
methods have attracted attention due to their much lower annotation costs
compared to fully-supervised segmentation. Leveraging reinforcement learning
(RL) self-play, we propose a novel WSS method that gamifies image segmentation
of a ROI. We formulate segmentation as a competition between two agents that
compete to select ROI-containing patches until exhaustion of all such patches.
The score at each time-step, used to compute the reward for agent training,
represents likelihood of object presence within the selection, determined by an
object presence detector pre-trained using only image-level binary
classification labels of object presence. Additionally, we propose a game
termination condition that can be called by either side upon exhaustion of all
ROI-containing patches, followed by the selection of a final patch from each.
Upon termination, the agent is incentivised if ROI-containing patches are
exhausted or disincentivised if an ROI-containing patch is found by the
competitor. This competitive setup ensures minimisation of over- or
under-segmentation, a common problem with WSS methods. Extensive
experimentation across four datasets demonstrates significant performance
improvements over recent state-of-the-art methods. Code:
https://github.com/s-sd/spurl/tree/main/wss
