Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer

arXiv:2608.27162 · cond-mat.mtrl-sci, cs.CV, cs.LG · Submitted 2026-08-27 · Read on arXiv

cond-mat.mtrl-sci, cs.CV, cs.LG

Submitted: 2026-08-27

Updated: 2026-08-27

Terminology

• Foundation Model — 6× in this paper · explained in 3 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Anisotropy — 4× in this paper · explained in 3 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• European Conference on Computer Vision — 2× in this paper
• Foundation Models — 2× in this paper · explained in 7 episodes
• Mask Generation — 2× in this paper · explained in 2 episodes
• alpha — 2× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• DINOv3
• DINOv2: Learning Robust Visual Features without Supervision
• SAM 2: Segment Anything in Images and Videos

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