Towards cautious collective inference for object verification
It is by now generally accepted that reasoning about the relationships between objects (and object hypotheses) can improve the accuracy of object detection methods. Relations between objects allow to reject inconsistent hypotheses and reduce the uncertainty of the initial hypotheses. However, most methods to date reason about object relations in a relatively crude way. In this paper we propose an alternative using cautious inference. Building on ideas from Collective Classification, we favor the most confident hypotheses as sources of contextual information and give higher relevance to the object relations observed during training. Additionally, we propose to cluster the pairwise relations into relationships. Our experiments on part of the KITTI data benchmark and the MIT StreetScenes dataset show that both steps improve the performance of relational classifiers.