Weakly Supervised Fish Segmentation through Point-Supervised Tracking and Motion-Informed Language
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.
ProofPaper ↗
Key points
- Understanding the properties of fish populations is increasingly important.
- 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.
- Experiments on the DeepFish dataset show improvement in foreground mean intersection over union (mIoU) as compared to baseline models.
Sources (1)
- [1]Weakly Supervised Fish Segmentation through Point-Supervised Tracking and Motion-Informed LanguagearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 01:22 AM
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.
Understanding the properties of fish populations is increasingly important.
Extractive summary: sentences quoted from the sources.
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