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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 .

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 39l.jpg

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. Other Potential Matches Recent papers like Monkey: Image

(PDF) Image Matching across Wide Baselines: From Paper to Practice "39l.jpg" also appears in these areas:

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