Description page: This dataset consisted of crossly-cropped images of 68 object categories that were distributed across three levels of a category hierarchy. There were 48 subordinate-level categories, which were grouped into 16 basic-level categories, which were grouped into 4 superordinate-level categories. A categorical search task was used, and the participants were 26 Stony Brook University undergraduates.
Paper : (2019) Modelling attention control using a convolutional neural network designed after the ventral visual pathway, Visual Cognition,
Microwave-Clock Search Dataset (MCS)
Description page: Microwave Clock Search
Paper : Zelinsky, G., Yang, Z., Huang, L., Chen, Y., Ahn, S., Wei, Z., … & Hoai, M. (2019). Benchmarking Gaze Prediction for Categorical Visual Search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops
SBU-CES (Stony Brook University – Categorical and Exemplar Search)
Paper: Adeli, H., Vitu, F., & Zelinsky, G. J. (2017). A model of the superior colliculus predicts fixation locations during scene viewing and visual search. Journal of Neuroscience, 37(6), 1453-1467.
Stony Brook University Real-world Clutter Dataset (SBU-RwC90)
Description page: Image Clutter and Proto-object Segmentation
Paper: Yu, C. P., Hua, W. Y., Samaras, D., & Zelinsky, G. (2013). Modeling clutter perception using parametric proto-object partitioning. In Advances in neural information processing systems (pp. 118-126).