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In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets. The properties of their features remain, however, largely unstudied under the transfer perspective. In this work, we present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression...
This paper deals with image categorization from weak supervision, e.g. global image labels. We propose to improve the region selection performed in latent variable models such as Latent Support Vector Machine (LSVM) by leveraging human eye movement features collected from an eye-tracker device. We introduce a new model, Gaze Latent Support Vector Machine (G-LSVM), whose region selection during training...
In this paper, we present a system for image retrieval over a network of computer based on "ant-like" mobile-agents. Image databases are hosted on the network, and the user wants to find all the images matching a specific concept (cars, flower, Italy, etc...). Usually, content based image retrieval systems (CBIR) do not consider the dispertion of the data among the network. We train a SVM...
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