Neural Information Processing Systems http://nips.cc/

An algorithm for learning fast multiclass object detection cascades is introduced. It produces multi-resolution (MRes) cascades, whose early stages are binary target vs. non-target detectors that eliminate false positives, late stages multiclass classifiers that finely discriminate target classes, and middle stages have intermediate numbers of classes, determined in a data-driven manner. This MRes structure is achieved with a new structurally biased boosting algorithm (SBBoost). SBBost extends previous multiclass boosting approaches, whose boosting mechanisms are shown to implement two complementary data-driven biases: 1) the standard bias towards examples difficult to classify, and 2) a bias towards difficult classes. It is shown that structural biases can be implemented by generalizing this class-based bias, so as to encourage the desired MRes structure.

Despite the recent trend of increasingly large datasets for object detection, there still exist many classes with few training examples. To overcome this lack of train- ing data for certain classes, we propose a novel way of augmenting the training data for each class by borrowing and transforming examples from other classes. Our model learns which training instances from other classes to borrow and how to transform the borrowed examples so that they become more similar to instances from the target class. Our experimental results demonstrate that our new object detector, with borrowed and transformed examples, improves upon the current state-of-the-art detector on the challenging SUN09 object detection dataset.

An algorithm for learning fast multiclass object detection cascades is introduced. It produces multi-resolution (MRes) cascades, whose early stages are binary target vs. non-target detectors that eliminate false positives, late stages multiclass classifiers that finely discriminate target classes, and middle stages have intermediate numbers of classes, determined in a data-driven manner. This MRes structure is achieved with a new structurally biased boosting algorithm (SBBoost). SBBost extends previous multiclass boosting approaches, whose boosting mechanisms are shown to implement two complementary data-driven biases: 1) the standard bias towards examples difficult to classify, and 2) a bias towards difficult classes. It is shown that structural biases can be implemented by generalizing this class-based bias, so as to encourage the desired MRes structure. This is accomplished through a generalized definition of multiclass margin, which includes a set of bias parameters.