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Related Experiment Video

Updated: Feb 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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MTRAG: Multi-Target Referring and Grounding via Hybrid Semantic-Spatial Integration.

Yili Ren, Jinyang Du, Xi Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 16, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces MTRAG, a novel framework for pixel-level multi-target referring and grounding. MTRAG enhances scene understanding by effectively combining semantic and spatial information for improved vision-language tasks.

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    Last Updated: Feb 18, 2026

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Fine-grained visual referring and grounding are essential for scene understanding and vision-language applications.
    • Existing multimodal large language models (MLLMs) struggle with fine-grained multi-target scenarios.

    Purpose of the Study:

    • To propose MTRAG, a pixel-level framework for multi-target referring and grounding that addresses limitations in current MLLMs.
    • To enhance semantic-spatial collaboration for improved performance in complex visual tasks.

    Main Methods:

    • Introduced Channel Extension Mechanism (CEM) for global and multi-region feature extraction without additional region extractors.
    • Developed a grounding branch for pixel-level grounding and a Hybrid Adapter (HA) to fuse semantic and spatial features.
    • Curated MTRAG-D dataset and MTR-Bench benchmark for systematic evaluation of multi-target referring.

    Main Results:

    • MTRAG consistently outperforms strong baselines on both multi-target and single-target referring and grounding tasks.
    • The framework maintains competitive performance in image-level captioning.
    • Demonstrated effective semantic-spatial alignment through the Hybrid Adapter.

    Conclusions:

    • MTRAG offers a robust solution for pixel-level multi-target referring and grounding.
    • The proposed methods significantly advance the capabilities of MLLMs in fine-grained visual understanding.
    • MTRAG provides a valuable benchmark for future research in multi-target visual tasks.