Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion (2207.14172v2)

Abstract: The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection. However, DETR suffers from slow training convergence, which hinders its applicability to various detection tasks. We observe that DETR's slow convergence is largely attributed to the difficulty in matching object queries to relevant regions due to the unaligned semantics between object queries and encoded image features. With this observation, we design Semantic-Aligned-Matching DETR++ (SAM-DETR++) to accelerate DETR's convergence and improve detection performance. The core of SAM-DETR++ is a plug-and-play module that projects object queries and encoded image features into the same feature embedding space, where each object query can be easily matched to relevant regions with similar semantics. Besides, SAM-DETR++ searches for multiple representative keypoints and exploits their features for semantic-aligned matching with enhanced representation capacity. Furthermore, SAM-DETR++ can effectively fuse multi-scale features in a coarse-to-fine manner on the basis of the designed semantic-aligned matching. Extensive experiments show that the proposed SAM-DETR++ achieves superior convergence speed and competitive detection accuracy. Additionally, as a plug-and-play method, SAM-DETR++ can complement existing DETR convergence solutions with even better performance, achieving 44.8% AP with merely 12 training epochs and 49.1% AP with 50 training epochs on COCO val2017 with ResNet-50. Codes are available at https://github.com/ZhangGongjie/SAM-DETR .

Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion

The paper "Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion" presents an innovative approach to addressing a well-documented issue in the DEtection TRansformer (DETR) framework—its slow convergence during training. This paper builds upon the baseline established by DETR, a Transformer-based object detection method that eliminates many of the hand-crafted components present in traditional convolutional neural network (ConvNet)-based detectors, creating a fully end-to-end detection model. Despite these advantages, DETR is known for significantly slower convergence rates, which has been a barrier to wider application and efficiency.

Core Contributions

The primary contribution of this paper is the development of a novel plug-and-play module named Semantic-Aligned Matching DETR++ (SAM-DETR++). The authors identify that one major cause of slow convergence in DETR is the difficulty in appropriately matching object queries to relevant regions within the image. This arises due to unaligned semantics between object queries and the encoded image features. SAM-DETR++ tackles this challenge by aligning these semantics through a feature projection mechanism, which facilitates more efficient and effective matching in DETR's cross-attention modules.

Key Features of SAM-DETR++

1. Semantic-Aligned Matching:
   • The proposed SAM-DETR++ aligns the semantic space of object queries and encoded image features, reducing the complexity in the cross-attention module of DETR.
   • This alignment results in quicker convergence as it imposes a useful prior that allows object queries to focus on semantically similar regions, thus reducing the training time substantially.
2. Keypoint-Based Feature Representation:
   • SAM-DETR++ enhances representation by searching for and leveraging features from multiple keypoints that hold the most distinguishing semantics.
   • Attention mechanisms are naturally extended by evaluating attention weightages on these representative regions, thus improving the robustness and accuracy of object detection.
3. Multi-Scale Feature Fusion:
   • The mechanism is further extended to incorporate multi-scale feature fusion, allowing the model to effectively work with objects of varying scales within the image.
   • This approach significantly mitigates representation difficulties and adds to the convergence speed improvements achieved through semantic alignment.

Empirical Evaluation and Performance

The paper presents extensive empirical evidence from experiments conducted on the COCO 2017 dataset. SAM-DETR++ demonstrates highly competitive results, achieving superior convergence speed and better detection performance when compared to classical and contemporary object detectors such as Faster R-CNN and other DETR variations like Deformable DETR, and Conditional DETR. Notably, SAM-DETR++ achieves 44.8% average precision (AP) on the COCO val2017 dataset with only 12 epochs, outperforming the original DETR's 500 epoch results. The integration with strategies like SMCA-DETR and DN-DETR also exhibits improved performance gains, highlighting its compatibility and adaptability.

Implications and Future Directions

This work suggests practical advancements not only for Transformer-based object detection but broadly for machine learning models where efficient training is critical. The semantic alignment methodology has potential implications for other domains involving sequence-to-sequence tasks or any framework relying on cross-attention mechanisms. Furthermore, the multi-scale feature fusion extension posits direction for future exploration in scenarios with substantial diversity in object scales and contexts. Future research may further refine this integration into broader applications and assess generalization across different datasets and tasks in computer vision.

In conclusion, this paper makes a notable contribution by addressing an intrinsic inefficiency in one of the most promising object detection frameworks, thereby broadening its utility and efficacy in both academic and practical consequential applications.