Lot Machine: Multimodal Lot Extraction from Auction Catalogs

arXiv:2608.30510 · cs.CV, cs.AI, cs.CL, cs.DL · Submitted 2026-08-31 · Read on arXiv

cs.CV, cs.AI, cs.CL, cs.DL

Submitted: 2026-08-31

Updated: 2026-09-30

Comments: Accepted at the VISART Workshop (Computer Vision for Art Analysis), ECCV 2026. 19 pages, 6 figures, 5 tables. Supplementary material included as an appendix. Code, benchmark data, and prompt templates: https://github.com/mathiaszinnen/auction-lot-extraction

Code: https://github.com/mathiaszinnen/auction-lot-extraction

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

Terminology

• Accuracy — 17× in this paper · explained in 1 episode
• Provenance — 16× in this paper · explained in 4 episodes
• Fidelity — 12× in this paper · explained in 2 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Mixture of Experts — 5× in this paper · explained in 2 episodes
• Data Sovereignty — 3× in this paper · explained in 2 episodes
• Edge Deployment — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Human-in-the-Loop — 3× in this paper · explained in 3 episodes
• Key Information Extraction — 3× in this paper
• Mixture of Experts (MoE) — 3× in this paper · explained in 5 episodes
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Trace — 2× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Average Normalized Levenshtein Similarity — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Character Error Rate — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Optical Character Recognition — 1× in this paper
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Standardized Benchmarks — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Qwen Technical Report
• PaddleOCR 3.0 Technical Report
• Mixtral of Experts
• ANLS* -- A Universal Document Processing Metric for Generative Large Language Models
• olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
• PaliGemma 2: A Family of Versatile VLMs for Transfer
• Gemma: Open Models Based on Gemini Research and Technology
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• Efficient Guided Generation for Large Language Models

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