[Submitted on 21 Oct 2026]
Abstract:Saliency and scanpath models are developed almost exclusively on natural photographs, yet paintings are composed by artists to guide the viewer’s eye and differ from photographs in texture, colour and abstraction. We introduce avatt, a free-viewing eye-tracking dataset on nImages{} paintings spanning sixteen art movements and five centuries, with nScanpaths{} scanpaths and nFixations{} fixations from nParticipants{} participants (spPerImageMin–spPerImageMax{} observers per painting). For every painting we release individual scanpaths with fixation timing and eye–screen distance, fixation maps, saliency maps smoothed at one degree of visual angle, temporal saliency videos and rich art metadata. We benchmark classic and deep saliency models against human agreement, revealing substantial headroom for art-aware models, and rank 44 scanpath-similarity measures, including a new language-free family of image-encoder metrics built on ten CNN, ViT and self-supervised encoders. With controls that remove viewing biases, we show that faces are fixated faceEnrich$times$ more than expected and within half a second, that local structure draws the eye, that composition rules fitted from image content predict where people look, and that art style shapes how much viewers agree. Dataset, code and a Python package are available at: this https URL.
Submission history
From: Mohamed Amine Kerkouri [view email]
[v1] Wed, 21 Oct 2026 22:02:01 UTC (25,997 KB)
