Weakly Supervised Object Detection Using Complementary Learning and Instance Clustering

Supervised object detection schemes use fully annotated training data, which is fairly expensive to constitute. Whereas, weakly supervised object detection (WSOD) uses only image-level annotations for training which are much simpler to acquire. WSOD is a challenging task since it aims to learn object localization and detection with image-level labels. In line with this assertion, in this paper, we present an end-to-end framework for WSOD based on discriminative feature learning. We use the objectness technique to get initial proposals from the images. Afterwards, two complementary networks are trained in parallel to obtain discriminative image features, which are channel-wise concatenated with the features of the third network. We name this classification network designed for discriminative feature learning as fused complementary network. This network learns the proposals enclosing whole object instances by complementary features which ultimately learns to predict the high probabilities for whole objects than proposals containing only object parts. Clustering is then hierarchically performed on the region proposals. Our clustering method, named instance clustering, first performs inter-class clustering followed by iterative intra-class clustering using intersection-over-union metric to obtain spatially adjacent cluster members corresponding to each object instance. In each intra-class clustering iteration, the high scoring proposal is set as centroid from each intra-class cluster. Experiments are conducted on PASCAL VOC2007 and PASCAL VOC2012 datasets. Both qualitative and quantitative results have shown improved WSOD performance on these benchmarks.