Abstract Knowledge distillation (KD) has witnessed its powerful capability in learning
compact models in object detection. Previous KD methods for object detection mostly focus …

Abstract Knowledge distillation (KD) has shown potential for learning compact models in
dense object detection. However, the commonly used softmax-based distillation ignores the …

Real-world object detection models should be cheap and accurate. Knowledge distillation
(KD) can boost the accuracy of a small, cheap detection model by leveraging useful …

Two head structures (ie fully connected head and convolution head) have been widely used
in R-CNN based detectors for classification and localization tasks. However, there is a lack …

Dense object detectors rely on the sliding-window paradigm that predicts the object over a
regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted to …

Abstract 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 …

Previous knowledge distillation (KD) methods for object detection mostly focus on feature
imitation instead of mimicking the prediction logits due to its inefficiency in distilling the …

We define the object detection from imagery problem as estimating a very large but
extremely sparse bounding box dependent probability distribution. Subsequently we identify …

One-stage detector basically formulates object detection as dense classification and
localization (ie, bounding box regression). The classification is usually optimized by Focal …

We propose a single-stage detection framework that jointly tackles the problem of multi-
scale object detection and class imbalance. Rather than designing deeper networks, we …