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Multi-View Visual Question Answering with Active Viewpoint Selection.

Yue Qiu1,2, Yutaka Satoh1,2, Ryota Suzuki2

  • 1Graduate School of Science and Technology, University of Tsukuba, Tsukuba 305-8577, Japan.

Sensors (Basel, Switzerland)
|April 23, 2020
PubMed
Summary

This study introduces an iterative visual question answering (VQA) framework for improved human-robot interaction (HRI). The method efficiently gathers visual data from multiple viewpoints, reducing necessary observations for accurate scene understanding.

Keywords:
deep learninghuman–robot interactionreinforcement learningthree-dimensional (3D) visionvisual question answering

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Conventional visual question answering (VQA) relies on single-view images, limiting its applicability in real-world scenarios like human-robot interaction (HRI).
  • HRI necessitates handling occluded scenes and varying camera angles, challenges not adequately addressed by single-view VQA.
  • Multi-view settings are crucial for VQA in HRI, but efficiently acquiring necessary views remains a challenge.

Purpose of the Study:

  • To propose a novel framework for iterative visual question answering (VQA) that actively observes scenes from multiple viewpoints.
  • To enhance VQA performance in complex environments by enabling iterative scene observation until sufficient information is gathered.
  • To reduce the number of required observations and camera movements for effective VQA in multi-view settings.

Main Methods:

  • Developed a framework for iterative scene observation to gather visual information for answering questions.
  • Implemented an active observation strategy to select optimal viewpoints for data acquisition.
  • Constructed a new multi-view VQA dataset using real-world images.

Main Results:

  • The proposed framework achieved performance comparable to state-of-the-art methods in question answering.
  • Significantly reduced the number of required observation viewpoints compared to existing approaches.
  • Demonstrated the framework's ability to learn optimal viewpoint selection, minimizing camera movements.
  • Achieved high accuracy (94.01%) on an unseen real image dataset.

Conclusions:

  • The iterative, multi-view VQA framework effectively addresses limitations of single-view methods in complex environments like HRI.
  • The framework's active viewpoint selection optimizes data acquisition, leading to improved efficiency and reduced computational cost.
  • The developed dataset and framework show strong potential for advancing VQA research in real-world applications.