Adapting an object detector by considering the worst case: A conservative approach

The performance of an offline-trained classifier can be improved on-site by adapting the classifier towards newly acquired data. However, the adaptation rate is a tuning parameter affecting the performance gain substantially. Poor selection of the adaptation rate may worsen the performance of the original classifier. To solve this problem, we propose a conservative model adaptation method by considering the worst case during the adaptation process. We first construct a random cover of the set of the adaptation data from its partition. For each element in the cover (i.e. a portion of the whole adaptation data set), we define the cross-entropy error function in the form of logistic regression. The element in the cover with the maximum cross-entropy error corresponds to the worst case in the adaptation. Therefore we can convert the conservative model adaptation into the classic min-max optimization problem: finding the adaptation parameters that minimize the maximum of the cross-entropy errors of the cover. Taking the object detection as a testbed, we implement an adapted object detector based on binary classification. Under different adaptation scenarios and different datasets including PASCAL, ImageNet, INRIA, and TUD-Pedestrian, the proposed adaption method achieves significant performance gain and is compared favorably with the state-of-the-art adaptation method with the fine tuned adaptation rate. Without the need of tuning the adaptation rates, the proposed conservative model adaptation method can be extended to other adaptive classification tasks.