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Of late, weakly supervised object detection is with great importance inobject recognition. Based on deep learning, weakly supervised detectors haveachieved many promising results. However, compared with fully superviseddetection, it is more challenging to train deep network based detectors in aweakly supervised manner. Here we formulate weakly supervised detection as aMultiple Instance Learning (MIL) problem, where instance classifiers (objectdetectors) are put into the network as hidden nodes. We propose a novel onlineinstance classifier refinement algorithm to integrate MIL and the instanceclassifier refinement procedure into a single deep network, and train thenetwork end-to-end with only image-level supervision, i.e., without objectlocation information. More precisely, instance labels inferred from weaksupervision are propagated to their spatially overlapped instances to refineinstance classifier online. The iterative instance classifier refinementprocedure is implemented using multiple streams in deep network, where eachstream supervises its latter stream. Weakly supervised object detectionexperiments are carried out on the challenging PASCAL VOC 2007 and 2012benchmarks. We obtain 47% mAP on VOC 2007 that significantly outperforms theprevious state-of-the-art.

Multiple Instance Detection Network with Online Instance Classifier Refinement