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Elenco Pubblicazioni - Publications (1288)

L. Vadicamo, F. Carrara, A. Cimino, S. Cresci, F. Dell’Orletta, F. Falchi, M. Tesconi: “Cross-Media Learning for Image Sentiment Analysis in the Wild”, Proceedings of the 5th Workshop on Web-scale Vision and Social Media (VSM), ICCV 2017

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Much progress has been made in the field of sentiment analysis in the past years. Researchers relied on textual data for this task, while only recently they have started in- vestigating approaches to predict sentiments…
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G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Meghini, C.Vairo: "Deep Learning for Decentralized Parking Lot Occupancy Detection", ESWA Expert Systems with Applications Journal 72, 327-334

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A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional…
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F. Carrara, F. Falchi, R. Caldelli, G. Amato, R. Fumarola, R. Becarelli, “Detecting adversarial example attacks to deep neural networks”, Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing (CBMI), June 2017

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Deep learning has recently become the state of the art in many computer vision applications and in image classification in particular. However, recent works have shown that it is quite easy to create adversarial examples,…
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G. Amato, F. Carrara, F. Falchi, C. Gennaro: “Efficient Indexing of Regional Maximum Activations of Convolutions using Full-Text Search Engines”, Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval (ICMR), June 2017

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In this paper, we adapt a surrogate text representation technique to develop efficient instance-level image retrieval using Regional Maximum Activations of Convolutions (R-MAC). R-MAC features have recently showed outstanding performance in visual instance retrieval. However,…
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F. Carrara, A. Esuli, T. Fagni, F. Falchi, A.M. Fernández: "Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions", Neu-IR '16 SIGIR non-archival Workshop on Neural Information Retrieval

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In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as…
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