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Related Experiment Videos

Foundation Models based Scene Graph Generation.

Shuzhou Sun, Jing Liu, Li Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 13, 2026
    PubMed
    Summary

    This study introduces a novel framework for Scene Graph Generation (SGG) using Foundation Models (FMs). The FMSGG approach enhances zero-shot recognition and mitigates bias in visual scene understanding.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Scene Graph Generation (SGG) traditionally struggles with model bias and zero-shot recognition due to data limitations.
    • Foundation Models (FMs) show promise in improving downstream tasks through extensive pretraining.

    Purpose of the Study:

    • To propose a Foundation Model-based Scene Graph Generation (FMSGG) framework.
    • To leverage FMs to address challenges in SGG, including tail relationships and zero-shot triplets.
    • To mitigate inefficiencies, relationship ambiguity, and dual-bias in SGG.

    Main Methods:

    • Isolating relationship embeddings from FM image features using relationship prompts.
    • Augmenting text prompts to improve relationship description distinctiveness.
    • Implementing triplet-aware logit adjustment and adaptive evidential smoothing to combat bias.

    Main Results:

    • Achieved state-of-the-art mean recall rate and zero-shot recall rate.
    • Demonstrated an optimal balance between mean recall rate and recall rate.
    • Successfully leveraged FMs to enhance SGG performance.

    Conclusions:

    • The FMSGG framework effectively utilizes Foundation Models for improved Scene Graph Generation.
    • The proposed methods successfully address key challenges in SGG, including bias and zero-shot performance.
    • FMSGG offers a promising direction for advancing structured semantic scene representation.