Datasets:

Amine-CV
/

MODG

Self-contained subset for the GazeAgent human-vs-MLLM visual-exploration study. Everything needed to run the experiment on this subset is in this folder — no access to the full COCO-Search18 download or the COCO annotations is required.

images/TP/<category>/<id>.jpg          141 target-present stimulus images
images/TA/<category>/<id>.jpg          144 target-absent stimulus images
human_fixations/fixations_tp.json      human scanpaths for the TP images (all subjects)
human_fixations/fixations_ta.json      human scanpaths for the TA images (all subjects)
image_ids_tp.json / image_ids_ta.json  frozen id lists + per-image strata (ecc / clutter)
subset_manifest.json                   seed, thresholds, shortfalls — how it was drawn
config.yaml                            the knobs used to build it

Counts

TP: 141 images, 1410 human scanpaths
TA: 144 images, 1440 human scanpaths
10 human subjects per image.

Coordinate space (IMPORTANT)

All gaze was recorded on images resized to 1680x1050 (zero-padded, aspect ratio preserved). Fixation X/Y are pixels in that frame. Normalize to [0,1] as x / 1680, y / 1050.

human_fixations schema (raw COCO-Search18, lossless; one record per image x subject)

name, subject, task (target category), condition (present|absent), bbox [x,y,w,h] (TP only — the target), X[], Y[], T[] (fixation x / y / duration sequences, incl. the initial centre fixation), length, correct, RT (TP) / fixOnTarget (TA), split.

image_ids_* schema (one record per selected image)

image_id, coco_id, target_category, condition, split, n_subjects, object_count, clutter_bin (low|high), stratum; TP additionally has bbox, eccentricity, ecc_bin.

Provenance

Pool: validation split of COCO-Search18 (test-split fixations are withheld upstream, so validation is the held-out evaluation set).
Seed: 0. Clutter = number of COCO-2017 instance annotations per image.
Stratification: TP = category x eccentricity x clutter; TA = category x clutter; thresholds are per-category medians.
Regenerate: python scripts/build_subset.py && python scripts/export_subset.py.

Cite the source dataset

Yang, Z., Huang, L., Chen, Y., Wei, Z., Ahn, S., Zelinsky, G., Samaras, D., & Hoai, M. (2020). Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning. CVPR.

COCO-Search18 — Stratified Working Subset (standalone)

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