aalquwayfili/Dense-Set

Dense-Set

Dense-Set is a curated benchmark of visually dense scenes for text-to-image retrieval evaluation. It provides challenging subsets extracted from COCO and Flickr30K, focusing on crowded images with multiple object instances and underrepresented, low-attention classes.

This dataset is published alongside:

LARE: Low-Attention Region Encoding for Text–Image Retrieval ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA) — [Workshop Page](https://qanta-org.github.io/competition/2026/icml/) Project Page | Code

Dataset Samples

[image] Each image is re-captioned to explicitly describe rare or low-attention objects (highlighted in red), shifting focus away from dominant scene context.

Construction

Dense-Set was built through a three-stage pipeline designed to surface objects that standard vision-language models overlook:

1. 1.High-Density Filtering — Images processed with YOLO, ranked by total object count, top 10% retained as the high-density candidate pool.
2. 2.Rare-Class Isolation — Within the dense pool, object categories appearing exactly once per image are flagged as rare classes, corresponding to small or visually subordinate objects.
3. 3.Re-captioning — Rare-class detections occupying >15% of the image are filtered out. BLIP-2 is prompted with class-aware templates to explicitly describe the remaining underrepresented objects, producing fine-grained captions that shift focus away from dominant scene context.

Statistics

Usage

from datasets import load_dataset

coco_ds   = load_dataset("aalquwayfili/Dense-Set", "coco")
flickr_ds = load_dataset("aalquwayfili/Dense-Set", "flickr30k")

print(coco_ds["test"][0])

Acknowledgements

Dense-Set is built on images from COCO and Flickr30K.

Citation

@inproceedings{alquwayfili2026lare,
  title={LARE: Low-Attention Region Encoding for Text--Image Retrieval},
  author={Abdulmalik Alquwayfili and Faisal Almeshal and Jumanah Almajnouni
          and Leena Alotaibi and Faisal Alhajari and Mohammed Alkhrashi
          and Alreem Almuhrij and Abdullah Aldwyish and Raied Aljadaany
          and Huda Alamri and Muhammad Kamran J. Khan},
  booktitle={ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA)},
  year={2026},
  eprint={2606.18885},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://openreview.net/forum?id=42bo30qeLe}
}