Public articles linked to the same research event.
arXiv Starting from the BIOMEDICA corpus, the authors used DAB-DETR-based subfigure detection, Qwen2.5-VL-32B-Instruct subcaption extraction, and Qwen2.5-14B-Instruct summarization of inline context to curate and release OPEN-PMC-18M, a dataset of 18 million subfigure-text pairs, then trained vision-language encoders evaluated on 6 retrieval and 19 zero-shot classification tasks across radiology, microscopy, and visible light photography, reporting an average recall of 21.64, a 27% relative improvement over the previous best model OPEN-PMC, and an overall zero-shot average F1 of 39.77.
Starting from the BIOMEDICA corpus, the authors used DAB-DETR-based subfigure detection, Qwen2.5-VL-32B-Instruct subcaption extraction, and Qwen2.5-14B-Instruct summarization of inline context to curate and release OPEN-PMC-18M, a dataset of 18 million subfigure-text pairs, then trained vision-language encoders evaluated on 6 retrieval and 19 zero-shot classification tasks across radiology, microscopy, and visible light photography, reporting an average recall of 21.64, a 27% relative improvement over the previous best model OPEN-PMC, and an overall zero-shot average F1 of 39.77.
Starting from the BIOMEDICA corpus, the authors used DAB-DETR-based subfigure detection, Qwen2.5-VL-32B-Instruct subcaption extraction, and Qwen2.5-14B-Instruct summarization of inline context to curate and release OPEN-PMC-18M, a dataset of 18 million subfigure-text pairs, then trained vision-language encoders evaluated on 6 retrieval and 19 zero-shot classification tasks across radiology, microscopy, and visible light photography, reporting an average recall of 21.64, a 27% relative improvement over the previous best model OPEN-PMC, and an overall zero-shot average F1 of 39.77.
Starting from the BIOMEDICA corpus, the authors used DAB-DETR-based subfigure detection, Qwen2.5-VL-32B-Instruct subcaption extraction, and Qwen2.5-14B-Instruct summarization of inline context to curate and release OPEN-PMC-18M, a dataset of 18 million subfigure-text pairs, then trained vision-language encoders evaluated on 6 retrieval and 19 zero-shot classification tasks across radiology, microscopy, and visible light photography, reporting an average recall of 21.64, a 27% relative improvement over the previous best model OPEN-PMC, and an overall zero-shot average F1 of 39.77.