Datasets:

AbdulmalekDS
/

Dense-Set

license: cc-by-4.0
configs:
  - config_name: coco
    data_files:
      - split: test
        path: coco/test-*
  - config_name: flickr30k
    data_files:
      - split: test
        path: flickr30k/test-*

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 CVPR 2026 — MULA Workshop Project Page | Code

Dataset Samples

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:

High-Density Filtering — Images processed with YOLO, ranked by total object count, top 10% retained as the high-density candidate pool.
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.
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

Dataset	Split	# Images	Avg. Objects	Avg. # Classes
COCO	Original Test Set	40,504	6.71	2.85
	High-Density Subset	4,050	21.63	4.82
	Dense-Set	3,089	21.63	5.47
Flickr30K	Original Test Set	31,783	6.73	2.48
	High-Density Subset	3,178	19.40	4.38
	Dense-Set	2,477	19.55	4.85

Usage

from datasets import load_dataset

coco_ds   = load_dataset("AbdulmalekDS/Dense-Set", "coco")
flickr_ds = load_dataset("AbdulmalekDS/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 Huda Alamri and Muhammad Kamran J Khan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2026}
}