Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR (2303.07335v1)

Abstract: Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance. Its success cannot be achieved without the re-introduction of multi-scale feature fusion in the encoder. However, the excessively increased tokens in multi-scale features, especially for about 75\% of low-level features, are quite computationally inefficient, which hinders real applications of DETR models. In this paper, we present Lite DETR, a simple yet efficient end-to-end object detection framework that can effectively reduce the GFLOPs of the detection head by 60\% while keeping 99\% of the original performance. Specifically, we design an efficient encoder block to update high-level features (corresponding to small-resolution feature maps) and low-level features (corresponding to large-resolution feature maps) in an interleaved way. In addition, to better fuse cross-scale features, we develop a key-aware deformable attention to predict more reliable attention weights. Comprehensive experiments validate the effectiveness and efficiency of the proposed Lite DETR, and the efficient encoder strategy can generalize well across existing DETR-based models. The code will be available in \url{https://github.com/IDEA-Research/Lite-DETR}.

An Analysis of Lite DETR: An Interleaved Multi-Scale Encoder for Efficient DETR

The paper "Lite DETR: An Interleaved Multi-Scale Encoder for Efficient DETR" proposes a novel framework aimed at enhancing the computational efficiency of DEtection TRansformer (DETR) models, which have gained significant traction in object detection tasks. The authors present Lite DETR, a streamlined version that ostensibly achieves near-optimal detection performance while markedly lowering the computational overhead traditionally associated with multi-scale feature handling in DETR frameworks.

Contributions and Methodology

The proposed Lite DETR framework innovates primarily through its redefinition of the encoder architecture, which they address by:

1. Interleaved Feature Updating: The authors introduce an interleaved mechanism for updating high-level and low-level features. The high-level features, which are fewer and more abstract, are prioritized for frequent updates to maintain the semantic integrity of the detection task. In contrast, the low-level features, which are structurally numerous and computationally expensive, are updated less frequently, thereby reducing the overall computational demand.
2. Key-aware Deformable Attention (KDA): To mitigate the limitations of traditional attention mechanisms which do not leverage explicit key comparisons, the authors propose an enhancement in the form of key-aware deformable attention. This approach allows for more reliable attention weight predictions when updating feature maps, especially in multi-scale contexts.
3. Reduction of Computational Complexity: The Lite DETR architecture effectively reduces the number of operations required—resulting in a reported 60% decrease in GFLOPs—while sustaining 99% of the original model’s performance. This design paradigm notably maintains competitive performance even when applied across different DETR-based models with varying compositional elements.

Empirical Evaluation

The empirical results presented in the paper corroborate the effectiveness of Lite DETR. The authors furnish comprehensive experimental results demonstrating that their model not only maintains competent performance in terms of mean Average Precision (mAP) but also does so with significantly lower computational expenditure compared to existing models like the original Deformable DETR. For instance, an improvement is noted where Lite-Deformable DETR achieves comparable detection scores while requiring substantially fewer computational resources. This was evident in tasks ranging from object detection to pose estimation across standard benchmarks like the MS COCO dataset.

Implications and Future Directions

From a practical standpoint, the introduction of Lite DETR presents significant implications for real-world applications where computational resources may be constrained. The reduction in GFLOPs translates into potential energy savings and speed-ups for edge devices executing object detection tasks without compromising the integrity of detection results.

From a theoretical perspective, the interleaved updating strategy proposes a novel lens through which multi-scale feature fusion can be optimized—a potential that could be explored in subsequent research, not only in object detection but also in other domains leveraging encoder-decoder architectures.

Future work might delve into optimizing runtime implementations alongside computational reductions as suggested in their concluding remarks, potentially unlocking further efficiencies. There could also be exploration into how such interleaved architectures perform under various data regimes or in transfer learning scenarios, broadening the applicability of these findings.

In conclusion, Lite DETR stands as a robust proposition for efficiently addressing the computational demands of DETR models through innovative architectural modifications, thereby opening new avenues for resource-efficient object detection capabilities. The insights and methodologies presented in this paper have the potential to influence ongoing and future explorations in the deployment of Transformer-based models in computation-constrained environments.