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COMPANY · ENTITY #5685

Vision Transformer (ViT)

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

LSA-PAST: parameter-efficient adapters with prototype transport for compositional incremental learning

The paper (arXiv:2610.27203v1) introduces LSA-PAST, a method that integrates LoRA-style, depth-specialized adapters into a frozen ViT for states, objects and compositions, and uses a rehearsal-free affine transform (PAST) to map past prototypes into the current adapter space so old prototypes remain compatible as new compositions arrive; experiments report state-of-the-art accuracy–forgetting trade-offs on compositional incremental learning benchmarks.

6.0

MODELS · 1 SOURCE · arXiv cs.AI

LoRA adaptation of DINOv3 ViT greatly improves SAS ATR; later refinements give no further gain

The paper adapts a DINOv3 Vision Transformer to underwater synthetic aperture sonar (SAS) automatic target recognition using a three-stage, parameter-efficient pipeline: Stage 1 applies Low-Rank Adaptation (LoRA) with a frozen backbone, Stage 2 adds hard-negative mining, and Stage 3 uses Supervised Contrastive Learning (SupCon). On at-sea SAS data with a mission-level geographic split and comparisons at 85% test recall repeated over three seeds, LoRA (rank 4) raised AUPRC from 0.300 to 0.679 ± 0.027 while training only 0.26% of weights; the hard-negative and SupCon stages showed no meaningful improvement versus matched controls.

6.0