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Instance-based attention: where could humans look first when searching for an object instance.

Changxin Gao1, Nong Sang, Rui Huang

  • 1National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China.

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Summary
This summary is machine-generated.

This study introduces an instance-based attention model to predict human visual search focus for object instance localization. The model enables realistic image synthesis by placing objects where attention is predicted to be highest.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Predicting human visual attention is crucial for tasks like image synthesis and robotic vision.
  • Existing models often lack the ability to generalize to object instances with varying scales and poses.

Purpose of the Study:

  • To develop an instance-based attention model for predicting initial human gaze in object search tasks.
  • To apply this attention model to synthesize realistic images by guiding object placement.

Main Methods:

  • The model learns configurational rules from large datasets of scene images with global scene representations.
  • It predicts attention focus for specific object instances, considering their scale and pose.
  • Image synthesis is achieved by inserting objects at locations identified as high-attention areas.

Main Results:

  • The proposed instance-based attention model effectively predicts human visual search patterns.
  • Experimental results show promising performance in guiding object placement for image synthesis.
  • The model demonstrates the ability to handle objects with diverse scales and poses.

Conclusions:

  • The instance-based attention model offers a novel approach to understanding and predicting visual search behavior.
  • Its application in image synthesis leads to more realistic and human-like object placement.
  • The model's effectiveness is validated through experimental evaluation.