Cascade R-CNN_ Delving into High Quality Object Detection.pptx

Cascade R-CNN - Delving into High Quality Object Detection

1. Cascade R-CNN: Delving into High Quality Object Detection 2022/4/6, Changjin Lee

2. Introduction A tricky challenge in object detection A detector trained with low IoU threshold produces noisy bounding boxes A detector trained with high IoU threshold (weirdly)shows degraded performance Low IoU threshold noisy bboxes High IoU threshold high quality bboxes X

3. Why high IoU leads to worse performance? Overfitting - The number of positive examples exponentially vanishes when trained with a high IoU threshold being more picky towards the bounding box quality Mismatch between the IoUs for which the detector is optimal and those of the input hypotheses during inference Suppose a detector is trained for IoU of 0.5 but if it’s asked for a competition where the criteria is 0.7 , there’s a mismatch. 0.6 0.6 0.69 0.8 0.9 0.6 0.68 IoU threshold: 0.7 detector But I was trained with IoU 0.5.. it’s time to test with IoU 0.7!

4. Motivations of Cascade R-CNN [1] A detector optimized at a single IoU level is not necessarily optimal at other levels [2] A detector can only have high quality predictions if presented with high quality proposals (e.g. from RPN) (Bbox IoU with GT from RPN) RPN

5. Motivations of Cascade R-CNN Just increasing IoU threshold doesn’t solve the problem. The distribution of bounding box quality from a proposal network is heavily imbalanced towards low quality . If you increase IoU threshold, a lot of examples are wiped out. -> resulting in overfitting 2. High quality detectors are only optimal for high quality proposals . A large . distribution gap between RPN and detection head leads to mismatch High quality proposals Low quality proposals Goal: mAP70 RPN Detection Head

6. Cascade R-CNN Cascade-RCNN “stages” are trained sequentially with increasing IoU thresholds , using the output of one stage to train the next, being more selective against close false positives Let a single detector to handle a single IoU! The output of a detector is a “ good distribution ” for training the next higher quality detector The same cascade procedure is also applied at inference Stage 1 (IoU 0.5 ) Stage 2 (IoU 0.6 ) Stage 3 (IoU 0.7 ) 0.3 0.7 0.3 H0 : RPN (proposal network) H1 : Detection Head C : Classification score B : Bounding box predictions RPN RPN Head

7. Summary of Target Problems [1] A detector optimized at a single IoU level is not necessarily optimal at other levels [2] A detector can only have high quality predictions if presented with high quality proposals (e.g. from RPN) -> large distribution gap b/w RPN and Detection Head [3] Overfitting - Vanishing positive examples [4] Mismatch between the IoUs for which the detector is optimal and those of the input hypotheses during inference

8. Stage 1 (IoU 0.5 ) Stage 2 (IoU 0.6 ) Stage 3 (IoU 0.7 ) [1] A detector optimized at a single IoU level is not necessarily optimal at other levels [2] A detector can only have high quality predictions if presented with high quality proposals (e.g. from RPN) -> large distribution gap b/w RPN and Detection Head [3] Overfitting - Vanishing positive examples [4] Mismatch between the IoUs for which the detector is optimal and those of the input hypotheses during inference

9. [1] A detector optimized at a single IoU level is not necessarily optimal at other levels [2] A detector can only have high quality predictions if presented with high quality proposals (e.g. from RPN) -> large distribution gap b/w RPN and Detection Head [3] Overfitting - Vanishing positive examples [4] Mismatch between the IoUs for which the detector is optimal and those of the input hypotheses during inference Stage 1 (IoU 0.5 ) Stage 2 (IoU 0.6 ) Stage 3 (IoU 0.7 ) resampling distribution to higher quality

10. [1] A detector optimized at a single IoU level is not necessarily optimal at other levels [2] A detector can only have high quality predictions if presented with high quality proposals (e.g. from RPN) -> large distribution gap b/w RPN and Detection Head [3] Overfitting - Vanishing positive examples [4] Mismatch between the IoUs for which the detector is optimal and those of the input hypotheses during inference Stage 1 (IoU 0.5 ) Stage 2 (IoU 0.6 ) Stage 3 (IoU 0.7 ) Inference

11. Performance

12. References https://arxiv.org/abs/1712.00726 https://deep-learning-study.tistory.com/605