COCO-Search18
COCO-Search18 is a laboratory-quality dataset of goal-directed behavior large enough to train deep-network models. It consists of the eye gaze behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding ~300,000 search fixations. COCO-Search18 is now part of the MIT/Tuebingen Saliency Benchmark, previously the MIT Saliency Benchmark but renamed to reflect the new group that will be managing the competition. As part of this re-organization, the benchmark will broaden its scope to go beyond purely spatial fixation prediction and even beyond the free-viewing task. COCO-Search18 is partly responsible for this broadening, and represents a significant expansion of the benchmark into goal-directed search behavior. The training, validation, and test images in COCO-Search18 are already freely available as part of COCO. Researchers are also free to see and use COCO-Search18’s training and validation search fixations, but the fixations on the test images are withheld. As part of a separate benchmark track, it will be possible to upload predictions and have them evaluated on the test dataset. In this initial stage of release, only fixations made on target-present search trials are available at this time (stay tuned for release of the target-absent fixations). We hope you enjoy using COCO-Search18!
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If you use COCO-Search18, please cite:
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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 193-202).
@InProceedings{Yang_2020_CVPR,
author = {Yang, Zhibo and Huang, Lihan and Chen, Yupei and Wei, Zijun and Ahn, Seoyoung and Zelinsky, Gregory and Samaras, Dimitris and Hoai, Minh},
title = {Predicting Goal-Directed Human Attention Using Inverse Reinforcement Learning},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
