Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
<|reference_start|>Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement: DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention, which is proven effective for improving performance but also introduces a heavy computational burden and high dependence on stable query selection. This paper demonstrates that suboptimal two-stage selection strategies result in scale bias and redundancy due to the mismatch between selected queries and objects in two-stage initialization. To address these issues, we propose hierarchical salience filtering refinement, which performs transformer encoding only on filtered discriminative queries, for a better trade-off between computational efficiency and precision. The filtering process overcomes scale bias through a novel scale-independent salience supervision. To compensate for the semantic misalignment among queries, we introduce elaborate query refinement modules for stable two-stage initialization. Based on above improvements, the proposed Salience DETR achieves significant improvements of +4.0% AP, +0.2% AP, +4.4% AP on three challenging task-specific detection datasets, as well as 49.2% AP on COCO 2017 with less FLOPs. The code is available at https://github.com/xiuqhou/Salience-DETR.<|reference_end|>
@article{hou2024salience,
    title={Salience DETR: Enhancing Detection Transformer with Hierarchical
  Salience Filtering Refinement},
    author={Xiuquan Hou, Meiqin Liu, Senlin Zhang, Ping Wei, Badong Chen},
    journal={arXiv preprint arXiv:2403.16131},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16131},
    primaryClass={cs.CV}
}