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Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper , we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not(More)
We consider the 'group motion segmentation' problem and provide a solution for it. The group motion segmenta-tion problem aims at analyzing motion trajectories of multiple objects in video and finding among them the ones involved in a 'group motion pattern'. This problem is motivated by and serves as the basis for the 'multi-object activity recognition'(More)
With unconstrained data acquisition scenarios widely prevalent, the ability to handle changes in data distribution across training and testing data sets becomes important. One way to approach this problem is through domain adaptation, and in this paper we primarily focus on the unsupervised scenario where the labeled source domain training data is(More)
While video-based activity analysis and recognition has received much attention, existing body of work mostly deals with single object/person case. Coordinated multi-object activities, or group activities, present in a variety of applications such as surveillance, sports, and biological monitoring records, etc., are the main focus of this paper. Unlike(More)
In pattern recognition and computer vision, one is often faced with scenarios where the training data used to learn a model has different distribution from the data on which the model is applied. Regardless of the cause, any distributional change that occurs after learning a classifier can degrade its performance at test time. Domain adaptation tries to(More)