[Submitted on 17 Sep 2026]
Title:LoRA Enhanced Contrastive Learning with SAS Vision Transformers
View a PDF of the paper titled LoRA Enhanced Contrastive Learning with SAS Vision Transformers, by Dan Zimmerman and 4 other authors
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Abstract:Automatic target recognition (ATR) with synthetic aperture sonar (SAS) supports advanced naval capabilities, but deep learning is constrained by scarce target imagery, background clutter, and human-in-the-loop assessment. We adapt DINOv3 Vision Transformer (ViT) models to underwater SAS ATR using a three-stage parameter-efficient framework. Stage 1 uses Low-Rank Adaptation (LoRA) while freezing the ViT backbone, bridging the gap between natural-image pretraining and underwater acoustic propagation. Stage 2 uses hard-negative mining to strengthen the decision boundary against acoustic mimics, including rocks and sediment formations resembling man-made targets. Stage 3 uses Supervised Contrastive Learning (SupCon) to separate target and clutter representations. We evaluate at-sea SAS data using a mission-level geographic split, compare all arms at 85 percent test recall, and repeat each comparison over three random seeds. LoRA accounts for the primary effect, increasing area under the precision-recall curve (AUPRC) from 0.300 to 0.679 +/- 0.027 using the same frozen backbone. Rank 4 achieves this result while training only 0.26 percent of weights. Neither refinement stage exceeds its matched control: hard-negative mining changes AUPRC by -0.0045 +/- 0.0119 versus an equal-size random curriculum, and SupCon changes AUPRC by +0.0002 +/- 0.0096 versus the preceding stage. These null results indicate that mining occurred on data the encoder had already fit and that supervised stages had already imposed most target-clutter geometry. One efficient adaptation stage is sufficient; stacked refinement is not.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.21061 [cs.AI]
(or arXiv:2609.21061v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.21061
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Frank Bobe III [view email] [v1] Thu, 17 Sep 2026 20:30:22 UTC (1,390 KB)
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