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Natural-Language-Driven Multimodal Representation Learning for Audio-Visual Scene-Aware Dialog System.

Yoonseok Heo1, Sangwoo Kang2, Jungyun Seo1

  • 1Department of Computer Science and Engineering, Sogang University, Seoul 04107, Republic of Korea.

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|September 28, 2023
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Summary
This summary is machine-generated.

This study introduces an audio-visual scene-aware dialog system for AI. It enhances human-like communication by integrating audio, visual, and textual data for better understanding and response generation.

Keywords:
audio-visual scene-aware dialog systemevent keyword driven multimodal representation learningmultimodal deep learning

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Multimedia Systems

Background:

  • Multimedia systems require AI for human-like communication.
  • Existing systems struggle with integrating auditory information and lack interpretability.
  • Multimodal representation learning has advanced but has limitations.

Purpose of the Study:

  • To develop an audio-visual scene-aware dialog system.
  • To improve AI's comprehensive understanding of audio-visual scenes.
  • To enhance the interpretability of AI reasoning in dialog systems.

Main Methods:

  • Proposed a novel audio-visual scene-aware dialog system.
  • Utilized explicit information from each modality fused into a language model.
  • Employed a transformer-based decoder for response generation in a multitask learning setting.
  • Implemented a response-driven temporal moment localization for interpretability.

Main Results:

  • The proposed model demonstrated superiority over baseline models in quantitative and qualitative evaluations.
  • Achieved robust performance using all three modalities (audio, visual, text).
  • Attained state-of-the-art performance in system response reasoning tasks.

Conclusions:

  • The novel system effectively integrates multimodal information for enhanced dialog.
  • The interpretability method provides evidence for system response generation.
  • The model shows significant advancements in audio-visual scene understanding and AI communication.