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Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced(More)
Classifying materials from their appearance is a challenging problem, especially if illumination and pose conditions are permitted to change: highlights and shadows caused by 3D structure can radically alter a sample’s visual texture. Despite these difficulties, researchers have demonstrated impressive results on the CUReT database which contains many(More)
We address a question answering task on real-world images that is set up as a Visual Turing Test. By combining latest advances in image representation and natural language processing, we propose Neural-Image-QA, an end-to-end formulation to this problem for which all parts are trained jointly. In contrast to previous efforts, we are facing a multi-modal(More)
We propose a method for automatically answering questions about images by bringing together recent advances from natural language processing and computer vision. We combine discrete reasoning with uncertain predictions by a multiworld approach that represents uncertainty about the perceived world in a bayesian framework. Our approach can handle human(More)
Recent proliferation of a cheap but quality depth sensor, the Microsoft Kinect, has brought the need for a challenging category-level 3D object detection dataset to the fore. We review current 3D datasets and find them lacking in variation of scenes, categories, instances, and viewpoints. Here we present our dataset of color and depth image pairs, gathered(More)
The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide(More)
Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization. In this paper, we introduce a kernelized version of(More)
Video data provides a rich source of information that is available to us today in large quantities e.g. from on-line resources. Tasks like segmentation benefit greatly from the analysis of spatio-temporal motion patterns in videos and recent advances in video segmentation has shown great progress in exploiting these addition cues. However, observing a(More)
0262-8856/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.imavis.2009.05.005 * Corresponding author. Address: IDIAP Research In Marconi 19, Martigny, Switzerland. Tel.: +41 277 217 E-mail addresses: bcaputo@idiap.ch (B. Caputo), Hayman), fritz@mis.tu-darmstadt.de (M. Fritz), joe@n URLs: http://www.idiap.ch/bcaputo (B. Caputo), h darmstadt.de/mfritz(More)