Relation DETR: Exploring Explicit Position Relation Prior for Object Detection (2407.11699v1)

Published 16 Jul 2024 in cs.CV

Abstract: This paper presents a general scheme for enhancing the convergence and performance of DETR (DEtection TRansformer). We investigate the slow convergence problem in transformers from a new perspective, suggesting that it arises from the self-attention that introduces no structural bias over inputs. To address this issue, we explore incorporating position relation prior as attention bias to augment object detection, following the verification of its statistical significance using a proposed quantitative macroscopic correlation (MC) metric. Our approach, termed Relation-DETR, introduces an encoder to construct position relation embeddings for progressive attention refinement, which further extends the traditional streaming pipeline of DETR into a contrastive relation pipeline to address the conflicts between non-duplicate predictions and positive supervision. Extensive experiments on both generic and task-specific datasets demonstrate the effectiveness of our approach. Under the same configurations, Relation-DETR achieves a significant improvement (+2.0% AP compared to DINO), state-of-the-art performance (51.7% AP for 1x and 52.1% AP for 2x settings), and a remarkably faster convergence speed (over 40% AP with only 2 training epochs) than existing DETR detectors on COCO val2017. Moreover, the proposed relation encoder serves as a universal plug-in-and-play component, bringing clear improvements for theoretically any DETR-like methods. Furthermore, we introduce a class-agnostic detection dataset, SA-Det-100k. The experimental results on the dataset illustrate that the proposed explicit position relation achieves a clear improvement of 1.3% AP, highlighting its potential towards universal object detection. The code and dataset are available at https://github.com/xiuqhou/Relation-DETR.

Authors (6)

Xiuquan Hou (2 papers)
Meiqin Liu (31 papers)
Senlin Zhang (17 papers)
Ping Wei (26 papers)
Badong Chen (83 papers)
Xuguang Lan (34 papers)

Citations (3)

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Summary

An Analysis of the Relation-DETR Methodology for Enhanced Object Detection

Introduction

The paper presents a comprehensive paper of the Relation-DETR, an approach designed to enhance the performance and convergence speed of Detection Transformers (DETR) through the incorporation of an explicit position relation prior. This novel approach addresses the well-documented issue of slow convergence in transformers, which is traditionally attributed to the self-attention mechanism's lack of structural bias. By introducing a position relation encoder, Relation-DETR aims to refine the self-attention process progressively and expedite training.

Methodology and Key Innovations

Relation-DETR differentiates itself from previous DETR approaches by integrating an explicit positional relation prior into the self-attention mechanism. The primary components of this methodology include:

Position Relation Encoder: This component computes pairwise interactions between bounding box predictions across decoder layers and represents them in a high-dimensional space using sinusoidal encoding. This effectively mitigates the scale and translation biases inherent in position information learned implicitly.
Progressive Attention Refinement: The proposed method applies the relation encoder output progressively across multiple layers, enhancing the attention refinement process and thereby producing more accurate bounding box predictions. This novel integration leverages position relations to prioritize bounding boxes that correlate positional attributes.
Contrast Relation Pipeline: To balance the often-competing needs for deduplication in predictions and adequate positive supervision, the authors propose an extended contrastive pipeline that capitalizes on position relationships to enhance non-duplication detection. This dual-query strategy (matching and hybrid queries) allows for efficient object detection by reinforcing correct hypotheses and negating redundant detections.

Results and Comparisons

Relation-DETR demonstrates superior performance across a range of benchmarks, including the COCO 2017 dataset, where it outperforms several state-of-the-art DETR variations. Specifically, Relation-DETR achieves a notable improvement of +2.0% in average precision (AP) compared to the prominent DINO model, attaining 51.7% AP in the 1× setting and 52.1% AP in the 2× configuration. These results are achieved with significantly faster convergence rates, achieving robustness in just a fraction of the typical epochs required by prior methods.

The proposed methodology also maintains comprehensive applicability as a plug-and-play component for DETR variations, underscoring its utility and versatility. This adaptability is validated by successful integrations with existing models, with reported improvements of up to 2.0% AP without necessitating extensive architecture changes.

Implications and Future Directions

Relation-DETR's integration of an explicit position relation prior distinguishes it as a notable advancement in the arena of detection transformers. The implications of this work extend to the enhancement of object detection tasks, particularly in scenarios involving small or densely packed objects—an area historically challenging due to the intricacies of feature association and bounding box deduplication.

The introduction of a universal object detection dataset, SA-Det-100k, based on the Relation-DETR framework, lays the groundwork for further exploration into more generalizable detection models that can seamlessly operate across diverse domains.

Future research avenues could focus on further optimizing the contrast pipeline for dynamic query generation and exploring additional biases (e.g., semantic) that could be integrated within similar frameworks. Moreover, the application of the Relation-DETR approach to multi-modal tasks potentially provides a broader application spectrum beyond conventional object detection, inviting interdisciplinary research into hybrid models that capitalize on visual and contextual information simultaneously.

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