# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 2 Related Work
### Approaches to Enhancing DETR: Performance vs. Efficiency  
#### 2.2.1 Performance-Oriented Enhancements  
Several studies have focused on improving DETR's performance by refining its attention mechanism, enhancing the informativeness of queries, and introducing novel learning techniques. Deformable-DETR [8] improved DETR's convergence speed and accuracy by modifying the attention mechanism to focus on specific regions of the image rather than attending to all features equally. Conditional-DETR [21] increased learning efficiency by  
\begin{table}
\begin{tabular}{l c c l} \hline \hline
**Method** & **Performance** & **Efficiency** & **Limitations of Previous Work** \\ \hline Deformable-DETR [8] & ✓ & ✓ & Achieves some efficiency, but query count remains high. \\ DAB-DETR [9] & ✓ & X & Improves spatial alignment, but high query usage. \\ DN-DETR [10], DINO [2] & ✓ & X & Enhances robustness, but adds computational overhead on training phase. \\ H-DETR [11], Co-DETR [3] & ✓ & X & Improves training, increased query count and complexity. \\ RT-DETR [5] & ✓ & ✓ & Enhances encoder efficiency, decoder query optimization lacking. \\ Rank-DETR [7] & ✗ & ✓ & Limits initial queries,no dynamic adjustment. \\ DDO [7] & ✗ & ✓ & Reduces redundancy, violates end-to-end principle. \\ SQ-DETR (Ours) & ✓ & ✓ & Overcomes limitations, layer-wise query reduction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of DETR improvement approaches with an emphasis on motivation. ✓ indicates presence; X indicates absence.  
ensuring that each object query contained information about a specific region, allowing the model to refine object predictions more effectively. Other works, such as Anchor-DETR [22] and DAB-DETR [9], introduced anchor-based query mechanisms to improve the spatial alignment of object predictions, leading to faster convergence.  
Beyond improvements in attention mechanisms and query representation, several studies have explored denoising strategies and one-to-many matching to further enhance DETR's learning efficiency. DN-DETR [10] introduced a denoising training approach in which noisy queries are systematically added and removed during training, improving model robustness against variations in object locations. DINO [2] extended this idea by incorporating contrastive denoising training, mixed query selection, and a look-forward-twice mechanism, leading to significant performance gains. To improve training efficiency while preserving DETR's end-to-end inference structure, H-DETR [11] proposed hybrid matching, which combines one-to-one matching for confident objects with one-to-many matching for uncertain objects, ensuring a balanced learning process. Meanwhile, Co-DETR [3] adopted a multi-head assignment strategy, where multiple detection heads independently generate different object assignments, effectively leveraging one-to-many supervision across diverse feature representations.  
While these performance-oriented approaches have successfully improved DETR's accuracy and convergence speed, many of them achieve these gains by increasing the number of queries, which ultimately leads to higher computational overhead. Consequently, these methods do not address DETR's inefficiency in resource-constrained environments, necessitating further research on computationally efficient solutions.