Multiple Instance Detection Network with Online Instance Classifier Refinement
This work addresses the challenge of training object detectors without location annotations, offering a more efficient solution for computer vision applications, though it is incremental in improving existing weakly supervised detection frameworks.
The paper tackles weakly supervised object detection by formulating it as a Multiple Instance Learning problem and proposes an online instance classifier refinement algorithm integrated into a deep network, achieving 47% mAP on PASCAL VOC 2007, which significantly outperforms previous state-of-the-art methods.
Of late, weakly supervised object detection is with great importance in object recognition. Based on deep learning, weakly supervised detectors have achieved many promising results. However, compared with fully supervised detection, it is more challenging to train deep network based detectors in a weakly supervised manner. Here we formulate weakly supervised detection as a Multiple Instance Learning (MIL) problem, where instance classifiers (object detectors) are put into the network as hidden nodes. We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i.e., without object location information. More precisely, instance labels inferred from weak supervision are propagated to their spatially overlapped instances to refine instance classifier online. The iterative instance classifier refinement procedure is implemented using multiple streams in deep network, where each stream supervises its latter stream. Weakly supervised object detection experiments are carried out on the challenging PASCAL VOC 2007 and 2012 benchmarks. We obtain 47% mAP on VOC 2007 that significantly outperforms the previous state-of-the-art.