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Related Experiment Videos

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

Xiuquan Hou, Meiqin Liu, Shaoyi Du

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 29, 2026
    PubMed
    Summary
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    This study enhances DETR (DEtection TRansformer) performance by introducing position relation embeddings to improve attention mechanisms. The Relation-DETR+ framework boosts convergence and efficiency for various dense prediction tasks.

    Area of Science:

    • Computer Vision
    • Deep Learning
    • Artificial Intelligence

    Background:

    • Object detection models like DETR (DEtection TRansformer) suffer from slow convergence due to self-attention lacking structural input bias.
    • Existing methods struggle to effectively integrate spatial relationships, limiting performance in dense prediction tasks.

    Purpose of the Study:

    • To propose a novel scheme, Relation-DETR+, for enhancing the convergence and performance of DETR-based models.
    • To address the slow convergence issue by incorporating explicit position relation priors into the attention mechanism.
    • To develop a unified framework capable of handling multiple dense prediction tasks including object detection, semantic segmentation, instance segmentation, and panoptic segmentation.

    Main Methods:

    • Incorporation of position relation prior as attention bias to augment object detection.

    Related Experiment Videos

  • Introduction of an encoder for constructing position relation embeddings for progressive attention refinement.
  • Extension of the DETR pipeline into a contrastive relation pipeline to manage prediction conflicts.
  • Alleviation of pattern collapse in multi-layer relations via explicit layer-wise encoding and gated relation modulation.

    • Main Results:

    • Relation-DETR+ demonstrates superior performance and learning efficiency compared to state-of-the-art methods like DINO and Mask-DINO under similar training schedules.
    • The proposed relation encoder acts as a plug-and-play component, improving various DETR-like methods.
    • Extensive experiments on diverse datasets validate the effectiveness of the approach for unified dense prediction tasks.
    • Introduction of SA-Det-100k, a large-scale class-agnostic detection dataset, highlighting the potential of explicit position relations for universal object detection.

    Conclusions:

    • The proposed Relation-DETR+ framework effectively enhances DETR performance by integrating explicit position relations, leading to improved convergence and efficiency.
    • The approach offers a unified solution for multiple dense prediction tasks within a single framework.
    • The relation encoder is a versatile component applicable to a wide range of DETR-based architectures, advancing the field of computer vision.