mlsanityData to 7 Oct 2026

SALYPATH: A Deep-Based Architecture for visual attention prediction

arXiv 2107.00559 ↗First posted 29 Jun 2021cs.CVcited by 7

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Abstract (arXiv)

Human vision is naturally more attracted by some regions within their field of view than others. This intrinsic selectivity mechanism, so-called visual attention, is influenced by both high- and low-level factors; such as the global environment (illumination, background texture, etc.), stimulus characteristics (color, intensity, orientation, etc.), and some prior visual information. Visual attention is useful for many computer vision applications such as image compression, recognition, and captioning. In this paper, we propose an end-to-end deep-based method, so-called SALYPATH (SALiencY and scanPATH), that efficiently predicts the scanpath of an image through features of a saliency model. The idea is predict the scanpath by exploiting the capacity of a deep-based model to predict the saliency. The proposed method was evaluated through 2 well-known datasets. The results obtained showed the relevance of the proposed framework comparing to state-of-the-art models.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 29 Jun 2021.

Builds on

17 of 40 references.

References · 17

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  3. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  4. 2016What do different evaluation metrics tell us about saliency models?cited by 184
  5. 2015Attention to Scale: Scale-aware Semantic Image Segmentationcited by 169
  6. 2016Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Modelcited by 91
  7. 2020Unified Image and Video Saliency Modelingcited by 74
  8. 2017SalGAN: Visual Saliency Prediction with Generative Adversarial Networkscited by 63
  9. 2014Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNetcited by 58
  10. 2016A Deep Multi-Level Network for Saliency Predictioncited by 54
  11. 2016DeepGaze II: Reading fixations from deep features trained on object recognitioncited by 54
  12. 2017Human Pose Regression by Combining Indirect Part Detection and Contextual Informationcited by 52
5 more
  1. 2016Shallow and Deep Convolutional Networks for Saliency Predictioncited by 42
  2. 2018PathGAN: Visual Scanpath Prediction with Generative Adversarial Networkscited by 32
  3. 2017SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumescited by 29
  4. 2019Deep Saliency Models : The Quest For The Loss Functioncited by 8
  5. 2016OpenSalicon: An Open Source Implementation of the Salicon Saliency Modelcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 0; 2025: 0; 2026: 3.

7 papers.

  1. 2022A domain adaptive deep learning solution for scanpath prediction of paintingscited by 6
  2. 2022Deep-based quality assessment of medical images through domain adaptationcited by 4
  3. 2022An Inter-observer consistent deep adversarial training for visual scanpath predictioncited by 2
  4. 2026What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metriccited by 2
  5. 2021A Simple and efficient deep Scanpath Predictioncited by 1
  6. 2026SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptationcited by 1
  7. 2026OpenVAM: Open-World Visual Attention Modeling with VLMscited by 0