AVAtt：一个用于绘画视觉注意的自由观看眼动数据集与基准
AVAtt: A Free-Viewing Eye-Tracking Dataset and Benchmark for Visual Attention on Paintings
arXiv:2610.30481v1 公告类型：cross 摘要：显著性和扫视路径模型几乎完全是在自然照片上开发的，而绘画是艺术家为引导观者视线而构图的，在纹理、色彩和抽象程度上都与照片不同。我们推出 \avatt，这是一个自由观看的眼动追踪数据集，涵盖十六个艺术流派、跨越五个世纪的 \nImages{} 幅绘画，包含来自 \nParticipants{} 名参与者的 \nScanpaths{} 条扫视路径和 \nFixations{} 次注视（每幅画有 \spPerImageMin--\spPerImageMax{} 名观察者）。对于每幅画，我们发布带有注视时间和眼—屏距离的个体扫视路径、注视图、以一度视角平滑的显著性图、时序显著性视频以及丰富的艺术元数据。我们将经典和深度显著性模型与人类一致性进行基准比较，揭示了艺术感知模型仍有很大提升空间，并对 44 种扫视路径相似性度量进行了排名，其中包括一个基于十种 CNN、ViT 和自监督编码器构建的、无需语言的新图像编码器度量族。通过去除观看偏差的对照实验，我们表明人脸被注视的次数比预期多 \faceEnrich$\times$，且发生在半秒之内；局部结构吸引视线；从图像内容拟合出的构图规则能预测人们看向何处；艺术风格影响观者之间的一致程度。数据集、代码和 Python 包可在以下网址获取：https://huggingface.co/datasets/Amine-CV/AVAtt。
把绘画作为刺激材料发布眼动数据集，并给出44种扫视路径相似度指标的排名，可供研究视觉注意的研究者直接取用。
arXiv:2610.30481v1 Announce Type: cross 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: https://huggingface.co/datasets/Amine-CV/AVAtt.
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来源：arXiv · Image and Video Processing · arxiv.org