Self-correction Optimization for Interleaved Multimodal Generation

Key points

• However, generating interleaved image--text content remains challenging, as it requires tightly integrated multimodal understanding and generation capabilities.1
• In this work, we propose self-correction optimization (SCO), an effective training-free method for consistent interleaved generation.1
• Specifically, the new-event constraint promotes temporal consistency across image--text sequences, while the state-preserving constraint maintains the coherence of visual subjects throughout subsequent generation steps.1
• Experiments on challenging interleaved multimodal generation benchmarks demonstrate significant improvements in temporal coherence and visual-subject preservation.1

Sources (1)

• [1]Self-correction Optimization for Interleaved Multimodal Generation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:51 PM

  Multimodal large language models (MLLMs) have made significant progress in visual understanding and generation.

  However, generating interleaved image--text content remains challenging, as it requires tightly integrated multimodal understanding and generation capabilities.

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