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Predicting Task-Driven Attention via Integrating Bottom-Up Stimulus and Top-Down Guidance.

Zhixiong Nan, Jingjing Jiang, Xiaofeng Gao

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 24, 2021
    PubMed
    Summary

    This study introduces a new deep learning model for predicting task-driven attention (TDAttention) in daily activities. The model effectively integrates visual cues and task information for more accurate attention prediction.

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    Area of Science:

    • Computer Vision
    • Cognitive Science
    • Artificial Intelligence

    Background:

    • Task-free attention models are common, but task-driven attention (TDAttention) prediction in daily scenarios remains under-explored.
    • Human attention is influenced by both external stimuli (bottom-up) and internal goals (top-down).

    Purpose of the Study:

    • To develop a cognitively-inspired deep neural network for predicting TDAttention in real-world human activities.
    • To address the gap in research concerning TDAttention prediction.

    Main Methods:

    • Extracted bottom-up features like human pose and motion from image sequences.
    • Embedded coarse- and fine-grained task information as top-down features.
    • Fused bottom-up and top-down features to guide TDAttention prediction.

    Main Results:

    • The proposed model demonstrated effectiveness in TDAttention prediction on re-annotated public datasets.
    • Ablation studies confirmed the contribution of individual model components.
    • Comparative analysis showed superior performance against existing models.

    Conclusions:

    • The cognitively-explanatory deep neural network effectively predicts TDAttention by integrating bottom-up and top-down information.
    • The model offers a promising approach for understanding and predicting human attention in dynamic, task-oriented environments.