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Q-GrAM: Fine-Grained Image-Text Retrieval via Grouped Query Routing and Conditional Query Modulation.

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InstructSee: Instruction-Aware and Feedback-Driven Multimodal Retrieval with Dynamic Query Generation.

Guihe Gu1,2,3, Yuan Xue1,2,3, Zhengqian Wu1,2,3

  • 1National Engineering Research Center for Multimedia Software (NERCMS), Wuhan 430072, China.

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Summary

This study introduces an instruction-aware framework for cross-modal retrieval, enhancing large language models (LLMs) to better understand user intent from complex instructions for improved visual-language alignment.

Keywords:
cross-modal retrievaldynamic query refinementlarge language models (LLMs)multimodal representation learningsemantic reasoning

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Cross-modal retrieval aims to bridge different data types, notably aligning images with text.
  • Current methods face challenges in interpreting complex or evolving user instructions for accurate retrieval.
  • Understanding implicit user intent in multimodal queries remains a significant research gap.

Purpose of the Study:

  • To develop a novel cross-modal representation learning framework.
  • To enhance the ability of systems to capture user intent from natural language instructions.
  • To improve the adaptability and accuracy of multimodal retrieval systems.

Main Methods:

  • Proposed an instruction-aware dynamic query generation mechanism.
  • Integrated semantic reasoning capabilities of large language models (LLMs).
  • Dynamically constructed and iteratively refined query representations based on instructions and user feedback.

Main Results:

  • The framework significantly improved retrieval accuracy and adaptability on standard benchmarks.
  • Outperformed existing fixed-query baseline methods.
  • Demonstrated enhanced cross-modal alignment and generalization capabilities.

Conclusions:

  • The proposed framework effectively infers and adapts to implicit retrieval intent.
  • LLM-augmented dynamic query generation is crucial for complex instruction-based cross-modal retrieval.
  • The method offers a promising direction for more intuitive and accurate multimodal information access.