When Does Geometric View Synthesis Help Wine Label Retrieval? A Public One-Shot Benchmark Across Self-Supervised and Vision-Language Backbones

arXiv:2609.33359 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/EdenHuang/winesensed-one-shotbenchmark

Terminology

• Benchmark — 56× in this paper · explained in 2 episodes
• Accuracy — 42× in this paper · explained in 1 episode
• Adaptation — 28× in this paper · explained in 1 episode
• Fine-Tuning — 20× in this paper · explained in 5 episodes
• Frozen model — 17× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Grounding — 2× in this paper · explained in 3 episodes
• Segment Anything Model — 2× in this paper
• Segment Anything Model (SAM) — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Hierarchical Classification — 1× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Bridging the Catalog-to-Real Gap: Scalable Product Recognition via Multi-Stage Contrastive Learning

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