Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

Understand

We present Sparse R-CNN, a purely sparse method for object detection in images.

• Existing works on object detection heavily rely on dense object candidates, such as $k$ anchor boxes pre-defined on all grids of image feature map of size $H\times W$.
• In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
• By eliminating $HWk$ (up to hundreds of thousands) hand-designed object candidates to $N$ (e.g.