Reading: Cascade R-CNN — Delving into High Quality Object Detection (Object Detection)

Original article was published on Deep Learning on Medium

Reading: Cascade R-CNN — Delving into High Quality Object Detection (Object Detection)

In this story, Cascade R-CNN, by UC San Diego, is briefly described. Prior deep learning object detectors’ performance tends to degrade with increasing the IoU (Interaction over Union) thresholds. They usually suffer from two main factors:

  • Overfitting during training, due to exponentially vanishing positive samples, i.e. lot of positive samples are gone when IoU threshold increases.
  • Inference-time mismatch between the IoUs for which the detector is optimal and those of the input hypotheses. e.g.: training at higher(lower) IoU threshold but test at lower(higher) IoU threshold.

In this paper, Cascade R-CNN, by extending Faster R-CNN, is proposed to solve the above problems. And it is published in 2018 CVPR over 450 citations. (Sik-Ho Tsang @ Medium)