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Deep saliency models learn low-, mid-, and high-level features to predict scene attention.

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Deep saliency models predict human gaze by prioritizing high-level scene meaning and low-level image saliency. Different models show unique feature weighting patterns, suggesting a need to analyze model internals beyond performance benchmarks.

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Deep saliency models are state-of-the-art for predicting human gaze in real-world scenes.
  • Understanding how these models prioritize scene features is crucial for integrating them with cognitive theories of attention.
  • Existing research often focuses on performance benchmarking, neglecting the internal workings of these models.

Purpose of the Study:

  • To investigate the feature prioritization mechanisms of prominent deep saliency models (MSI-Net, DeepGaze II, SAM-ResNet).
  • To model the association between human attention, model output, and various scene features (low-, mid-, and high-level).
  • To understand how deep saliency models process scene information to predict human eye movements.

Main Methods:

  • Utilized a mixed-effects modeling approach on a large eye movement dataset.
  • Assessed the association between three deep saliency models and low-level image saliency.
  • Examined associations with mid-level features (contour symmetry, junctions) and high-level scene meaning.

Main Results:

  • All three deep saliency models showed the strongest associations with high-level scene meaning and low-level image saliency.
  • Qualitatively different feature weightings and interaction patterns were observed among the models.
  • The models' predictions are primarily driven by semantic content and basic visual attributes.

Conclusions:

  • Prominent deep saliency models learn to prioritize features related to high-level scene understanding and low-level saliency.
  • The distinct feature weighting patterns suggest model-specific processing strategies.
  • Emphasizes the importance of analyzing the internal mechanisms of deep saliency models, moving beyond performance metrics.