Dissimilarity Coefficient based Weakly Supervised Object Detection
This work addresses the problem of reducing annotation costs for object detection in computer vision, presenting an incremental improvement over existing weakly supervised methods.
The paper tackles weakly supervised object detection using only image-level labels by introducing a dissimilarity coefficient based probabilistic learning objective to model object location uncertainty, and demonstrates its efficacy on PASCAL VOC 2007 and 2012 datasets.
We consider the problem of weakly supervised object detection, where the training samples are annotated using only image-level labels that indicate the presence or absence of an object category. In order to model the uncertainty in the location of the objects, we employ a dissimilarity coefficient based probabilistic learning objective. The learning objective minimizes the difference between an annotation agnostic prediction distribution and an annotation aware conditional distribution. The main computational challenge is the complex nature of the conditional distribution, which consists of terms over hundreds or thousands of variables. The complexity of the conditional distribution rules out the possibility of explicitly modeling it. Instead, we exploit the fact that deep learning frameworks rely on stochastic optimization. This allows us to use a state of the art discrete generative model that can provide annotation consistent samples from the conditional distribution. Extensive experiments on PASCAL VOC 2007 and 2012 data sets demonstrate the efficacy of our proposed approach.