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  3. Visual Self-refinement For Autoregressive Models.
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  3. Visual Self-refinement For Autoregressive Models.

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Visual Self-Refinement for Autoregressive Models.

Jiamian Wang1, Ziqi Zhou1, Chaithanya Kumar Mummadi2

  • 1Rochester Institute of Technology.

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|February 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a refinement module to improve autoregressive models for vision-language tasks. The method enhances spatial correspondence and reduces errors in sequential generation, leading to more consistent outputs.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Autoregressive models are effective for sequential data, including vision-language tasks.
  • Challenges exist in modeling spatial visual data within sequential prediction frameworks.
  • Existing methods may suffer from suboptimal results due to the conflict between spatial and sequential data characteristics.

Purpose of the Study:

  • To propose a plug-and-play refinement module to enhance spatial correspondence modeling in autoregressive vision-language models.
  • To improve the quality and semantic consistency of generated visual sequences.
  • To mitigate error accumulation issues inherent in sequential generation.

Main Methods:

  • A novel refinement module is introduced as a post-pretraining step.
  • The module jointly refines all generated tokens within the autoregressive model.
  • It leverages global context and inter-token relationships for improved modeling.

    • Main Results:

    • The proposed method significantly enhances vision-language modeling capabilities.
    • It improves the quality of generated visual sequences.
    • Error accumulation in sequential generation is effectively mitigated.

    Conclusions:

    • The refinement module offers a practical solution for improving autoregressive vision-language models.
    • The approach successfully addresses the challenge of spatial-sequential data integration.
    • The method leads to more semantically consistent and higher-quality outputs in vision-language generation.