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

基于图像级标签的弱监督分割方法缺乏标签和感兴趣区域之间的明确对应关系，为此，我们提出了一种基于强化学习自我博弈的新型弱监督分割方法，通过将分割视为两个代理竞争选择包含感兴趣区域的图像块，从而实现对目标的定位。通过使用仅基于图像级二分类标签的目标存在检测器进行预训练，我们在每个时间步计算分数以计算代理训练的奖励，该分数表示选择中物体存在的可能性。此外，我们提出了一种游戏终止条件，当耗尽所有包含感兴趣区域的图像块后，任一方可以调用该条件，然后从中选择最终的图像块。通过这种竞争模式，我们确保最小化过度或不足分割的问题。在四个数据集上进行的广泛实验表明，与最新的最先进方法相比，我们的方法有显著的性能提升。

竞争像素：一种弱监督分割的自对弈算法