| # COCO-Search18 — Stratified Working Subset (standalone) |
|
|
| 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. |
|
|
| ## Contents |
| ``` |
| 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. |
|
|
