mlsanityData to 14 Oct 2026

SIGN: A Statistically-Informed Gaze Network for Gaze Time Prediction

arXiv 2501.17422 ↗First posted 29 Jan 2025cs.CV stat.APcited by 0

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Abstract (arXiv)

We propose a first version of SIGN, a Statistically-Informed Gaze Network, to predict aggregate gaze times on images. We develop a foundational statistical model for which we derive a deep learning implementation involving CNNs and Visual Transformers, which enables the prediction of overall gaze times. The model enables us to derive from the aggregate gaze times the underlying gaze pattern as a probability map over all regions in the image, where each region's probability represents the likelihood of being gazed at across all possible scan-paths. We test SIGN's performance on AdGaze3500, a dataset of images of ads with aggregate gaze times, and on COCO-Search18, a dataset with individual-level fixation patterns collected during search. We demonstrate that SIGN (1) improves gaze duration prediction significantly over state-of-the-art deep learning benchmarks on both datasets, and (2) can deliver plausible gaze patterns that correspond to empirical fixation patterns in COCO-Search18. These results suggest that the first version of SIGN holds promise for gaze-time predictions and deserves further development.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 29 Jan 2025.

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References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  5. 2016What do different evaluation metrics tell us about saliency models?cited by 185
  6. 2021Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentationcited by 127
  7. 2016Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Modelcited by 91
  8. 2014Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNetcited by 60
  9. 2019Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentationcited by 57
  10. 2023Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attentioncited by 38
  11. 2024GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpathscited by 15

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