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State-of-the-art visual recognition and detection systems increasingly relyon large amounts of training data and complex classifiers. Therefore it becomesincreasingly expensive both to manually annotate datasets and to keep runningtimes at levels acceptable for practical applications. In this paper, wepropose two solutions to address these issues. First, we introduce a weaklysupervised, segmentation-based approach to learn accurate detectors and imageclassifiers from weak supervisory signals that provide only approximateconstraints on target localization. We illustrate our system on the problem ofaction detection in static images (Pascal VOC Actions 2012), using human visualsearch patterns as our training signal. Second, inspired from thesaccade-and-fixate operating principle of the human visual system, we usereinforcement learning techniques to train efficient search models fordetection. Our sequential method is weakly supervised and general (it does notrequire eye movements), finds optimal search strategies for any given detectionconfidence function and achieves performance similar to exhaustive slidingwindow search at a fraction of its computational cost.

Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images