Consensus Focus for Object Detection and minority classes
Erik Isai Valle Salgado Chen Li Yaqi Han Linchao Shi Xinghui Li
Ensemble methods exploit the availability of a given number of classifiers or
detectors trained in single or multiple source domains and tasks to address
machine learning problems such as domain adaptation or multi-source transfer
learning. Existing research measures the domain distance between the sources
and the target dataset, trains multiple networks on the same data with
different samples per class, or combines predictions from models trained under
varied hyperparameters and settings. Their solutions enhanced the performance
on small or tail categories but hurt the rest. To this end, we propose a
modified consensus focus for semi-supervised and long-tailed object detection.
We introduce a voting system based on source confidence that spots the
contribution of each model in a consensus, lets the user choose the relevance
of each class in the target label space so that it relaxes minority bounding
boxes suppression, and combines multiple models' results without discarding the
poisonous networks. Our tests on synthetic driving datasets retrieved higher
confidence and more accurate bounding boxes than the NMS, soft-NMS, and WBF.