Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement

Temel Kavramlar

Hierarchical salience filtering refinement in Salience DETR improves object detection performance by addressing scale bias and redundancy issues in two-stage DETR-like detectors.

Özet

• Abstract:
  • DETR-like methods have improved detection performance.
  • Two-stage frameworks introduce computational burden and dependence on stable query selection.
  • Proposed hierarchical salience filtering refinement in Salience DETR addresses scale bias and redundancy issues.
• Introduction:
  • Object detection is crucial in computer vision.
  • DEtection TRansformer (DETR) has shown remarkable performance improvements.
• Related Work:
  • Various works explore transformer-based detectors like DETR.
• Salience DETR:
  • Adopts a two-stage pipeline with selective query encoding based on salience supervision.
  • Introduces hierarchical query filtering for better trade-off between efficiency and precision.
• Experiments and Discussions:
  • Salience DETR achieves significant improvements on task-specific datasets and COCO challenge.
• Ablation Studies:
  • Hierarchical query filtering, background embedding, and redundancy removal contribute to performance improvements.
• Scalability of Salience DETR:
  • Demonstrates superior performance on COCO dataset with lower computational cost.
• Visualization:
  • Shows salience confidence visualization and detection results of Salience DETR.

İstatistikler

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.

Alıntılar

"We propose hierarchical salience filtering refinement to address scale bias and redundancy issues in two-stage initialization."

Önemli Bilgiler Şuradan Elde Edildi

Salience DETR

by Xiuquan Hou,... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16131.pdf

Daha Derin Sorular

What implications does the hierarchical query filtering have for other transformer-based detectors

The implications of hierarchical query filtering extend beyond Salience DETR and can be beneficial for other transformer-based detectors. By selectively encoding a fraction of discriminative queries based on salience-guided supervision, this approach can help improve the efficiency and effectiveness of object detection models. This concept can be applied to transformers used in various computer vision tasks to enhance performance by focusing on relevant information while reducing computational burden.

How can the concept of salience-guided supervision be applied to other computer vision tasks beyond object detection

The concept of salience-guided supervision can be applied to a wide range of computer vision tasks beyond object detection. For instance, in image segmentation, salience could guide the model to focus on important regions for accurate delineation. In image classification, it could help prioritize features that are most relevant for identifying specific classes. Additionally, in facial recognition tasks, salience guidance could assist in recognizing key facial features with higher accuracy.

What are the potential challenges in implementing end-to-end solutions for redundancy removal in two-stage initialization

Implementing end-to-end solutions for redundancy removal in two-stage initialization may pose several challenges. One challenge is ensuring that the removal process does not inadvertently discard crucial information necessary for accurate predictions. Another challenge is designing an efficient algorithm that can identify and eliminate redundant queries without significantly increasing computational complexity or sacrificing performance. Additionally, maintaining a balance between removing redundancies and preserving essential details throughout the entire pipeline poses another potential challenge during implementation.