Abstract
Understanding the properties of fish populations is increasingly important. Analyzing fish length, width, and mass is integral to maintaining healthy stock in the aquaculture industry, while counting wild fish populations supports marine conservation and coastal reef restoration efforts. Deep learning-based segmentation models offer a promising means of obtaining such information at scale. Most of these models, however, require costly and time-consuming per-pixel segmentation labels for training, which can take up to two minutes per fish to annotate manually. To address this inefficiency, we propose a weakly supervised segmentation pipeline that requires only point-labeled data (one click per fish) to produce accurate segmentation masks. Our pipeline consists of four stages. First, a point-supervised tracker predicts point annotations for each frame. These points are then converted into motion-informed language expressions, which guide a language-conditioned segmentation model (LMPM++) to capture temporal consistency and produce candidate segmentation masks. Finally, optical flow and background subtraction are applied as post-processing filters to suppress noise from incorrect segmentations. Experiments on the DeepFish dataset show improvement in foreground mean intersection over union (mIoU) as compared to baseline models.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29047
