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Recently, many deep networks are proposed to learn hierarchical image representation to replace traditional hand-designed features. To enhance the ability of the generative model to tackle discriminative computer vision tasks (e.g. image classification), we propose a hierarchical deconvolutional network with two biologically inspired properties incorporated, i.e., non-negative sparsity and selectivity...
Context information has been widely studied for recognizing collective activities. Most existing works assume that all individuals in a single image share the same activity label. However, in many cases, multiple activities can be coexisted and serve as the context for each other in real-world scenarios. Based on this observation, we propose a novel approach to model both the intra-class and inter-class...
In order to detect the objects of interest, many different approaches have been proposed. One kind of popular approaches are based on template matching, which use a template of the object class to match the image at different positions. The matching can be computed using similarity measures such as the correlation coefficient. These approaches, although easy and robust, has the limitation of not containing...
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