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CE-BART: Cause-and-Effect BART for Visual Commonsense Generation.

Junyeong Kim1, Ji Woo Hong2, Sunjae Yoon2

  • 1Department of AI, Chung-Ang University, Seoul 06974, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces Cause-and-Effect BART (CE-BART), a novel approach for visual commonsense generation. CE-BART effectively generates cause-and-effect captions for images by considering relationships within and between modalities and captions.

Keywords:
AVSDVisualCOMETdeep learningvideo-grounded dialoguevisual commonsense generationvisual–language reasoning

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Humans infer past, present, and future events from images.
  • Visual commonsense generation aims to create three captions (before, current intent, after) for an image.
  • Existing methods struggle due to direct vision-language transformer use and independent caption generation.

Purpose of the Study:

  • To address limitations in current visual commonsense generation.
  • To propose a novel model, Cause-and-Effect BART (CE-BART), for improved visual commonsense generation.

Main Methods:

  • Developed CE-BART incorporating a structured graph reasoner.
  • The reasoner captures intra- and inter-modality relationships.
  • A cause-and-effect generator considers causal relations among inferences.

Main Results:

  • CE-BART achieved state-of-the-art (SOTA) performance on VisualCOMET and AVSD benchmarks.
  • Ablation studies confirmed performance gains.
  • Qualitative analysis demonstrated improved interpretability.

Conclusions:

  • CE-BART effectively generates cause-and-effect captions for images.
  • The model overcomes limitations of previous approaches by considering relational information.
  • CE-BART shows promise for advancing visual commonsense understanding.