This paper introduces a first-of-its-kind benchmark for fine-grained document retrieval in natural scenes. It includes a dataset of 41,000 document images (where files like 39l.jpg are part of the image corpus) paired with over 200,000 queries .
If the paper above does not match your specific context, "39l.jpg" also appears in these areas: 39l.jpg
Some researchers use these specific filenames in benchmarks for Image Matching across Wide Baselines to test how models handle different perspectives of the same object. (PDF) Image Matching across Wide Baselines: From Paper
(PDF) Image Matching across Wide Baselines: From Paper to Practice Other Potential Matches
Knowing if it contains a building , a document , or a product will help identify the exact research citation.
Recent papers like Monkey: Image Resolution and Text Label Are Important Things for CVPR 2024 use high-resolution image sets to improve visual understanding in Large Language Models.
The study highlights that OCR-free models perform better when queries involve visual, non-text elements, and that models pre-trained on image-text contrastive learning tasks (like CLIP ) show superior accuracy. Other Potential Matches