MS-DETR: Efficient DETR Training with Mixed Supervision (2401.03989v1)

Abstract: DETR accomplishes end-to-end object detection through iteratively generating multiple object candidates based on image features and promoting one candidate for each ground-truth object. The traditional training procedure using one-to-one supervision in the original DETR lacks direct supervision for the object detection candidates. We aim at improving the DETR training efficiency by explicitly supervising the candidate generation procedure through mixing one-to-one supervision and one-to-many supervision. Our approach, namely MS-DETR, is simple, and places one-to-many supervision to the object queries of the primary decoder that is used for inference. In comparison to existing DETR variants with one-to-many supervision, such as Group DETR and Hybrid DETR, our approach does not need additional decoder branches or object queries. The object queries of the primary decoder in our approach directly benefit from one-to-many supervision and thus are superior in object candidate prediction. Experimental results show that our approach outperforms related DETR variants, such as DN-DETR, Hybrid DETR, and Group DETR, and the combination with related DETR variants further improves the performance.

Overview of MS-DETR: Efficient DETR Training with Mixed Supervision

The paper "MS-DETR: Efficient DETR Training with Mixed Supervision" introduces an optimization of the Detection Transformer (DETR) architecture, which is a prominent end-to-end object detection framework. The primary contribution of the paper is a novel training scheme that enhances efficiency by combining mixed supervision into DETR's existing candidate generation procedure. This modified approach, termed MS-DETR, strategically implements a blend of one-to-one and one-to-many supervision, eschewing the need for additional decoder branches or object queries often seen in other variants.

Key Contributions

1. Mixed Supervision Strategy: The authors propose using a combination of one-to-one and one-to-many supervision to improve the efficiency of the candidate generation process within DETR. The mixed supervision is applied directly to the primary decoder's object queries, leading to superior object candidate prediction without necessitating additional architectural components.
2. Comparative Performance: MS-DETR demonstrates superior performance over existing DETR variants such as DN-DETR, Hybrid DETR, and Group DETR. The empirical results are compelling, showing significant improvements in mean Average Precision (mAP) with no additional computational burden during inference.
3. Efficient Implementation: The proposed MS-DETR framework is designed to maintain computational efficiency by leveraging existing resources within the primary decoder, thus optimizing both memory and computation during training while retaining simplicity in the model design.

Detailed Insights

DETR's fundamental strategy involves transforming object detection into a direct set prediction problem, which benefits from transformers' capabilities in handling sequence data. However, traditional DETR training lacks direct supervision for the multitude of object candidates generated, relying instead on promoting a single candidate per ground-truth object through one-to-one supervision. This often slows down training convergence.

MS-DETR innovatively addresses this by adding a mixed supervision layer, where one-to-many supervision directly enhances the object queries in the primary decoder. This approach contrasts with techniques like Group DETR and Hybrid DETR, which utilize parallel decoder branches and extra object queries, incurring additional resource costs. By focusing the supervision on existing decoder components, MS-DETR exerts a direct influence on candidate accuracy, improving inference without expanding the model's complexity.

In their experiments, the authors substantiate these claims by showcasing improved detection results across various DETR baselines. For instance, on Deformable DETR with 300 primary queries, MS-DETR achieved an increase of 3.7% mAP over the baseline in a 12-epoch training scenario. Additionally, the method synergizes well with existing one-to-many supervision approaches, facilitating further mAP gains when combined.

Theoretical and Practical Implications

Theoretically, MS-DETR contributes to the understanding of how mixed supervision can be effectively integrated into transformer-based architectures for object detection. The insights gained from this research may inform future designs of efficient and scalable object detection models.

On a practical level, the MS-DETR approach enhances the applicability of DETR and its variants in settings where computational resources are at a premium, such as mobile and edge devices, without sacrificing detection performance. This characteristic is crucial as it opens avenues for deploying robust object detection systems in real-time applications across various domains.

Speculations on Future Developments

Future developments could focus on further fine-tuning mixed supervision techniques, exploring adaptive supervision strategies that dynamically adjust supervision levels based on object complexity or scene dynamics. Additionally, the integration of MS-DETR with other advanced vision tasks and domains, such as 3D object detection or autonomous navigation, could yield further significant performance improvements.

In conclusion, MS-DETR introduces a strategic enhancement to the DETR framework, yielding considerable efficiency and performance gains. This work represents a valuable advance in the field of object detection, particularly for practitioners seeking optimized model training without added complexity.