Anchor DETR: Query Design for Transformer-Based Object Detection

Understand

In this paper, we propose a novel query design for the transformer-based object detection.

• In previous transformer-based detectors, the object queries are a set of learned embeddings.
• However, each learned embedding does not have an explicit physical meaning and we cannot explain where it will focus on.
• It is difficult to optimize as the prediction slot of each object query does not have a specific mode.