# DETR

In dense tiny-object detection, dynamic query designs previously entangled how many queries are decoded with which queries are kept, obscuring the source of gains. BUTR reframes dynamic query as allocating decoder query budget by object density: it sums encoder proposal confidences into a density proxy and maps it linearly to a per-image query count, keeping standard Top-k initialization without learned modules or heuristic filtering. On AI-TOD-v2, BUTR-L reaches 35.7 AP with 316 queries on average, versus 35.2 AP and 600 queries for same-scale D-FINE-L.

In dense tiny-object detection, dynamic query designs previously entangled how many queries are decoded with which queries are kept, obscuring the source of gains. BUTR reframes dynamic query as allocating decoder query budget by object density: it sums encoder proposal confidences into a density proxy and maps it linearly to a per-image query count, keeping standard Top-k initialization without learned modules or heuristic filtering. On AI-TOD-v2, BUTR-L reaches 35.7 AP with 316 queries on average, versus 35.2 AP and 600 queries for same-scale D-FINE-L.

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.