Visual In-Context Learning for Large Vision-Language Models

@inproceedings{Zhou2024VisualIL,
  title={Visual In-Context Learning for Large Vision-Language Models},
  author={Yucheng Zhou and Xiang Li and Qianning Wang and Jianbing Shen},
  booktitle={Annual Meeting of the Association for Computational Linguistics},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:267750174}
}
This work introduces a novel Visual In-Context Learning method comprising Visual Demonstration Retrieval, Intent-Oriented Image Summarization, and Intent-Oriented Demonstration Composition, which retrieves images via ''Retrieval&Rerank'' paradigm, summarises images with task intent and task-specific visual parsing, and composes language-based demonstrations that reduce token count and alleviate cross-modal interaction problem.
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51 References

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