Berga, DavidFernández Vidal, Xosé RamónOtazu, XavierPardo López, Xosé Manuel2024-12-202024-12-202019D. Berga, X. R. F. Vidal, X. Otazu and X. M. Pardo, "SID4VAM: A Benchmark Dataset With Synthetic Images for Visual Attention Modeling," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 8788-8797, doi: 10.1109/ICCV.2019.00888978-1-7281-4803-82380-7504https://hdl.handle.net/10347/38255This is the author’s version of the work. The definitive version was published in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), available online at 10.1109/ICCV.2019.00888A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor popout type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-ofthe- art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/ Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets.eng© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/VisualizationTask analysisComputational modelingMeasurementGaze trackingPredictive modelsBenchmark testingbook part10.1109/ICCV.2019.00888open access