The paper "Cascade R-CNN: High Quality Object Detection and Instance Segmentation" by Zhaowei Cai and Nuno Vasconcelos addresses the challenges of high-quality object detection and instance segmentation. The authors propose a multi-stage object detection architecture called Cascade R-CNN, which is designed to improve the quality of detection hypotheses and detectors. The key idea is to train a sequence of detectors with increasing intersection over union (IoU) thresholds, where each detector's output is used as the training set for the next detector. This process progressively improves the quality of hypotheses, ensuring a large number of positive examples for all detectors and minimizing overfitting. The same cascade is applied during inference to match the quality of hypotheses with the detectors, enhancing detection accuracy. The Cascade R-CNN is evaluated on various datasets, including COCO, VOC, KITTI, CityPerson, and WideFace, achieving state-of-the-art performance on COCO and significant improvements on other datasets. The authors also extend the Cascade R-CNN to instance segmentation, achieving non-trivial improvements over the Mask R-CNN. The code for the Cascade R-CNN is made available to facilitate future research. The paper discusses the challenges of high-quality detection, the design of the Cascade R-CNN, and its comparison with previous works, including iterative bounding box regression and integral loss. Experimental results demonstrate the effectiveness of the Cascade R-CNN in achieving high-quality object detection and instance segmentation.The paper "Cascade R-CNN: High Quality Object Detection and Instance Segmentation" by Zhaowei Cai and Nuno Vasconcelos addresses the challenges of high-quality object detection and instance segmentation. The authors propose a multi-stage object detection architecture called Cascade R-CNN, which is designed to improve the quality of detection hypotheses and detectors. The key idea is to train a sequence of detectors with increasing intersection over union (IoU) thresholds, where each detector's output is used as the training set for the next detector. This process progressively improves the quality of hypotheses, ensuring a large number of positive examples for all detectors and minimizing overfitting. The same cascade is applied during inference to match the quality of hypotheses with the detectors, enhancing detection accuracy. The Cascade R-CNN is evaluated on various datasets, including COCO, VOC, KITTI, CityPerson, and WideFace, achieving state-of-the-art performance on COCO and significant improvements on other datasets. The authors also extend the Cascade R-CNN to instance segmentation, achieving non-trivial improvements over the Mask R-CNN. The code for the Cascade R-CNN is made available to facilitate future research. The paper discusses the challenges of high-quality detection, the design of the Cascade R-CNN, and its comparison with previous works, including iterative bounding box regression and integral loss. Experimental results demonstrate the effectiveness of the Cascade R-CNN in achieving high-quality object detection and instance segmentation.