Ritvik Rastogi

Dec 8, 2023

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Region Based Convolutional Neural Networks

Proposes a multi-stage approach where detectors are trained with progressively higher IoU thresholds, improving selectivity against false positives.
Extends Faster R-CNN to solve instance segmentation tasks, by adding a branch for predicting an object mask in parallel with the existing branch.
A region proposal network (RPN) and a Fast R-CNN detector, collaboratively predict object regions by sharing convolutional features.
Processes entire image through CNN, employs RoI Pooling to extract feature vectors from ROIs, followed by classification and BBox regression.
Uses selective search for region proposals, CNNs for feature extraction, SVM for classification followed by box offset regression.
Ritvik Rastogi

Ritvik Rastogi

Data Scientist, 2x Kaggle Expert