Beyond Average: Individualized Visual Scanpath Prediction Article Swipe
Xianyu Chen
,
Ming Jiang
,
Qi Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.12235
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.12235
Understanding how attention varies across individuals has significant scientific and societal impacts. However, existing visual scanpath models treat attention uniformly, neglecting individual differences. To bridge this gap, this paper focuses on individualized scanpath prediction (ISP), a new attention modeling task that aims to accurately predict how different individuals shift their attention in diverse visual tasks. It proposes an ISP method featuring three novel technical components: (1) an observer encoder to characterize and integrate an observer's unique attention traits, (2) an observer-centric feature integration approach that holistically combines visual features, task guidance, and observer-specific characteristics, and (3) an adaptive fixation prioritization mechanism that refines scanpath predictions by dynamically prioritizing semantic feature maps based on individual observers' attention traits. These novel components allow scanpath models to effectively address the attention variations across different observers. Our method is generally applicable to different datasets, model architectures, and visual tasks, offering a comprehensive tool for transforming general scanpath models into individualized ones. Comprehensive evaluations using value-based and ranking-based metrics verify the method's effectiveness and generalizability.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.12235
- https://arxiv.org/pdf/2404.12235
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394972014
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394972014Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.12235Digital Object Identifier
- Title
-
Beyond Average: Individualized Visual Scanpath PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-18Full publication date if available
- Authors
-
Xianyu Chen, Ming Jiang, Qi ZhaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.12235Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.12235Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2404.12235Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Machine learning, Computational biology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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