Automated Goldsmith's Mark Retrieval in Silverware

arXiv:2609.20509 · cs.CV, cs.DL · Submitted 2026-09-17 · Read on arXiv

cs.CV, cs.DL

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: Accepted at the VISART workshop, ECCV 2026. 18 pages, 8 figures, 1 table

Code: https://github.com/Atmickk/VISART2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Fine-Tuning — 9× in this paper · explained in 5 episodes
Provenance — 8× in this paper · explained in 3 episodes
Accuracy — 7× in this paper · explained in 1 episode
Feature Extraction — 6× in this paper · explained in 2 episodes
Embeddings — 4× in this paper · explained in 2 episodes
Adam — 2× in this paper · explained in 2 episodes
Clustering — 2× in this paper · explained in 2 episodes
Transformer — 2× in this paper · explained in 2 episodes
Ablation — 1× in this paper · explained in 1 episode
Ablation Studies — 1× in this paper · explained in 1 episode
Attention Mechanism — 1× in this paper · explained in 2 episodes
Computer Vision Annotation Tool — 1× in this paper
Content-Based Image Retrieval — 1× in this paper
Core — 1× in this paper · explained in 2 episodes
Cosine Similarity — 1× in this paper · explained in 3 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Regularization — 1× in this paper · explained in 3 episodes
Robustness — 1× in this paper · explained in 5 episodes
Self-Supervised Learning — 1× in this paper · explained in 7 episodes
Semantic Gap — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

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