Deformable DETR

2000년 도입 · 논문 35편에서 사용

Deformable DETR is an object detection method that aims mitigates the slow convergence and high complexity issues of DETR. It combines the best of the sparse spatial sampling of deformable convolution, and the relation modeling capability of Transformers. Specifically, it introduces a deformable attention module, which attends to a small set of sampling locations as a pre-filter for prominent key elements out of all the feature map pixels. The module can be naturally extended to aggregating multi-scale features, without the help of FPN.

출처: Deformable DETR: Deformable Transformers for End-to-End Object Detection

소개 논문: Deformable DETR: Deformable Transformers for End-to-End Object Detection