Cross-Domain Object Recognition Using Object Alignment

In this paper, we focus on the problem of cross-domain object recognition [4], which has long been one of the challenging problems in computer vision. This problem typically arises when training (source domain) and test (target domain) samples are drawn from different distributions. In the problem of object recognition, this case is usually caused by the situation that training and test samples are acquired under different sets of background, lighting, view point, resolution conditions, etc. One popular solution to the problem of cross-domain object recognition is minimizing the difference between the source and target distributions. Existing methods are devoted to minimizing that domain difference in a complex image space, which makes the problem hard to solve because of background influence, as shown in Figure 1 (a). Since the object and background are twisted in that image feature space, the discrepancy caused by background is difficult to eliminate, which makes it hard to learn optimal fS and fT for minimizing D( fS(XS), fT (XT )). To discount the influence of the background, we propose to minimize that difference using object alignment. As shown in Figure 1 (b), we minimize the domain difference by transferring to the feature space of aligned objects XS and XT , but not the image feature space having background influence. The key insight of our approach is that the difference between the source and target distributions can be reduced by discounting the influence from the ambiguous background. We define the semantic object as the object that occurs in all the images of one class. To discount the background influence, our primary goal is to automatically localize the semantic object so that the irrelevant background can be eliminated. Then based on the semantic object regions, we can learn an object detector that is robust to the influence of the irrelevant background and makes the crossdomain object recognition much easier than before. In addition, since our detectors are learned in a weakly supervised way, we utilize the classificaSource domain Selective search Object alignment — Topic discovery