Visual attention distribution operates via a two-component framework
Visual attention distribution is widely modeled and understood through a two-component framework, such as the classic distinction between goal-driven and stimulus-driven systems or architectures combining spatial and channel-wise attention.
The retrieved literature contains multiple studies across cognitive modeling and computer vision (e.g., papers 0, 1, 2, 5, 6, and 10) that explicitly rely on or investigate a two-component framework for visual attention (such as goal-driven vs. stimulus-driven, or spatial vs. channel-wise attention). There are no papers contradicting this premise.
Motonori Yamaguchi. Top-Down Contributions to Attention Shifting and Disengagement: A Template Model of Visual Attention. 2018. https://doi.org/10.31234/osf.io/tcdar
Paper [10] explicitly describes visual attention control through two separate systems or components (goal-driven and stimulus-driven).
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Yiwei Ma, Jiayi Ji, Xiaoshuai Sun, Yiyi Zhou, Yongjian Wu, Feiyue Huang, Rongrong Ji. Knowing What it is: Semantic-Enhanced Dual Attention Transformer. 2023. https://doi.org/10.1109/TMM.2022.3164787
Paper [0] utilizes a dual-attention framework combining spatial and channel-wise attention to model visual feature dependencies.
Aakansha Mishra, A. Anand, Prithwijit Guha. Dual Attention and Question Categorization-Based Visual Question Answering. 2023. https://doi.org/10.1109/TAI.2022.3160418
Paper [1] proposes a dual attention mechanism to capture enriched cross-modality representations in visual tasks.
Manahil Raza, Ruqayya Awan, R. M. S. Bashir, Talha Qaiser, N. Rajpoot. Dual attention model with reinforcement learning for classification of histology whole-slide images. 2023. https://doi.org/10.1016/j.compmedimag.2024.102466
Paper [2] implements a dual attention approach consisting of a soft attention model for regions of interest and a hard attention model for multi-resolution glimpses.
Hussein Samma, Ahmed Abubaker, Mubarak Aremu Badamasi, Mohammed Abdel-Nasser, Sami Elferik. Fusion of Visual Attention and Scene Descriptions With Deep Reinforcement Learning for AAV Indoor Autonomous Navigation. 2025. https://doi.org/10.1109/ACCESS.2025.3566428
Paper [5] employs a dual guidance framework with visual attention and scene-aware components for autonomous navigation.
M. Ahmad, Arshad Khan, Taimur Ali Khan, Bilal Ahmad. Dual Attention-Driven Optimized YOLOV5 Framework for Accurate Fall Detection in Visual Monitoring Systems. 2025. https://doi.org/10.62762/tis.2025.559776
Paper [6] integrates spatial and channel-wise attention mechanisms (dual attention) for accurate fall detection in visual monitoring.
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