WaterBox: Weakly supervised underwater instance segmentation and a new benchmark

Box-supervised instance segmentation has gained increasing attention due to its reliance on weak box annotations, which are considerably less expensive than pixel-wise mask annotations. Despite the advantage, existing methods in this category often struggle in complex underwater scenes, where degraded image quality causes foreground objects to become heavily entangled with the background. To address this issue, we propose WaterBox, a cost-effective box-supervised underwater instance segmentation method. Considering the intrinsic characteristics of underwater imaging, we introduce a novel pairwise loss function that leverages a mixed color affinity map with a dynamic threshold to effectively disambiguate foreground and background boundaries. Additionally, we devise a bounding box refinement strategy that generates tight and accurate bounding boxes for each instance, alleviating the negative impact of imprecise box annotations on segmentation performance. Furthermore, to fill in the gaps caused by data scarcity, we construct the first diver instance segmentation dataset, DSeg, which consists of 2000 underwater images with high-quality instance masks. Extensive experiments on two underwater datasets demonstrate the superiority of our approach over the state-of-the-art (SOTA) weakly supervised methods. The code and dataset will be made publicly available.