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

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Related Experiment Video

Updated: Jul 16, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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

Guihe Gu1, Huawei Li2, Hong Qin2

  • 1School of Computer Science, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces Q-GrAM, a novel method for fine-grained text-to-image retrieval. Q-GrAM improves search accuracy by structuring queries for detailed semantic understanding.

Keywords:
Q-Formerfine-grained retrievalgroup-aware late interactiongrouped query routingimage–text retrievaltext-to-image retrieval

Related Experiment Videos

Last Updated: Jul 16, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current image-text retrieval methods use global vectors, limiting fine-grained semantic understanding.
  • Textual queries with objects, attributes, and relations are challenging for existing models.

Purpose of the Study:

  • To propose Q-GrAM, an adaptation of BLIP-2 Q-Former for fine-grained text-to-image retrieval.
  • To enhance the retrieval of images based on detailed textual descriptions.

Main Methods:

  • Q-GrAM partitions query budget into semantically distinct groups.
  • A text-guided router assigns semantic demands, and conditional initialization modulates groups.
  • Group-aware late interaction scoring matches visual and textual tokens.

Main Results:

  • Q-GrAM demonstrates strong performance on MS-COCO 5K, Flickr30K, and Flickr30K-CFQ datasets.
  • Achieves superior text-to-image retrieval compared to global embedding and fine-grained matching methods.
  • Maintains competitive bidirectional retrieval performance.

Conclusions:

  • Structured, text-conditioned query specialization is effective for fine-grained retrieval.
  • Q-GrAM advances the capabilities of text-driven image search systems.