2017
Other
Open Access
Exploring epoch-dependent stochastic residual networks
Carrara F, Esuli A, Falchi F, Moreo Fernández A
The recently proposed stochastic residual networks selectively activate or bypass the layers during training, based on independent stochastic choices, each of which following a probability distribution that is fixed in advance. In this paper we present a first exploration on the use of an epoch-dependent distribution, starting with a higher probability of bypassing deeper layers and then activating them more frequently as training progresses. Preliminary results are mixed, yet they show some potential of adding an epoch-dependent management of distributions, worth of further investigation.
Carrara F, Esuli A, Falchi F, Moreo Fernández A
The recently proposed stochastic residual networks selectively activate or bypass the layers during training, based on independent stochastic choices, each of which following a probability distribution that is fixed in advance. In this paper we present a first exploration on the use of an epoch-dependent distribution, starting with a higher probability of bypassing deeper layers and then activating them more frequently as training progresses. Preliminary results are mixed, yet they show some potential of adding an epoch-dependent management of distributions, worth of further investigation.
See at:
arxiv.org 
| CNR IRIS | ISTI Repository | CNR IRIS 
2017
Conference article
Open Access
Efficient indexing of regional maximum activations of convolutions using full-text search engines
Amato G, Carrara F, Falchi F, Gennaro C
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, contrary to the activations of hidden layers adopting ReLU (Rectified Linear Unit), these features are dense. This constitutes an obstacle to the direct use of inverted indexes, which rely on sparsity of data. We propose the use of deep permutations, a recent approach for efficient evaluation of permutations, to generate surrogate text representation of R-MAC features, enabling indexing of visual features as text into a standard search-engine. The experiments, conducted on Lucene, show the effectiveness and efficiency of the proposed approach.DOI: 10.1145/3078971.3079035
Metrics:
Amato G, Carrara F, Falchi F, Gennaro C
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, contrary to the activations of hidden layers adopting ReLU (Rectified Linear Unit), these features are dense. This constitutes an obstacle to the direct use of inverted indexes, which rely on sparsity of data. We propose the use of deep permutations, a recent approach for efficient evaluation of permutations, to generate surrogate text representation of R-MAC features, enabling indexing of visual features as text into a standard search-engine. The experiments, conducted on Lucene, show the effectiveness and efficiency of the proposed approach.DOI: 10.1145/3078971.3079035
Metrics:
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ISTI Repository | dl.acm.org | doi.org | CNR IRIS | CNR IRIS
2017
Dataset
Metadata Only Access
T4SA: Twitter for Sentiment Analysis
Carrara F, Cimino A, Cresci S, Dell'Orletta F, Falchi F, Vadicamo L, Tesconi M
T4SA is intended for training and testing image sentiment analysis approaches. It contains little less than a million tweets, corresponding to about 1.5M images. We initially collected about 3.4M tweets corresponding to about 4M images. We classified the sentiment polarity of the texts (as described in Section 4) and we selected the tweets having the most confident textual sentiment predictions to build our Twitter for Sentiment Analysis (T4SA) dataset. The dataset is publicly available at: http://www.t4sa.it/
Carrara F, Cimino A, Cresci S, Dell'Orletta F, Falchi F, Vadicamo L, Tesconi M
T4SA is intended for training and testing image sentiment analysis approaches. It contains little less than a million tweets, corresponding to about 1.5M images. We initially collected about 3.4M tweets corresponding to about 4M images. We classified the sentiment polarity of the texts (as described in Section 4) and we selected the tweets having the most confident textual sentiment predictions to build our Twitter for Sentiment Analysis (T4SA) dataset. The dataset is publicly available at: http://www.t4sa.it/
See at:
CNR IRIS | www.t4sa.it
2017
Journal article
Open Access
Deep learning for decentralized parking lot occupancy detection
Amato G, Carrara F, Falchi F, Gennaro C, Meghini C, Vairo C
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 Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger.Source: EXPERT SYSTEMS WITH APPLICATIONS, vol. 72, pp. 327-334
DOI: 10.1016/j.eswa.2016.10.055
Metrics:
Amato G, Carrara F, Falchi F, Gennaro C, Meghini C, Vairo C
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 Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger.Source: EXPERT SYSTEMS WITH APPLICATIONS, vol. 72, pp. 327-334
DOI: 10.1016/j.eswa.2016.10.055
Metrics:
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | Expert Systems with Applications | CNR IRIS | CNR IRIS
2017
Conference article
Open Access
Detecting adversarial example attacks to deep neural networks
Carrara F, Falchi F, Caldelli R, Amato G, Fumarola R, Becarelli R
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, i.e., images intentionally created or modified to cause the deep neural network to make a mistake. They are like optical illusions for machines containing changes unnoticeable to the human eye. This represents a serious threat for machine learning methods. In this paper, we investigate the robustness of the representations learned by the fooled neural network, analyzing the activations of its hidden layers. Specifically, we tested scoring approaches used for kNN classification, in order to distinguishing between correctly classified authentic images and adversarial examples. The results show that hidden layers activations can be used to detect incorrect classifications caused by adversarial attacks.DOI: 10.1145/3095713.3095753
Metrics:
Carrara F, Falchi F, Caldelli R, Amato G, Fumarola R, Becarelli R
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, i.e., images intentionally created or modified to cause the deep neural network to make a mistake. They are like optical illusions for machines containing changes unnoticeable to the human eye. This represents a serious threat for machine learning methods. In this paper, we investigate the robustness of the representations learned by the fooled neural network, analyzing the activations of its hidden layers. Specifically, we tested scoring approaches used for kNN classification, in order to distinguishing between correctly classified authentic images and adversarial examples. The results show that hidden layers activations can be used to detect incorrect classifications caused by adversarial attacks.DOI: 10.1145/3095713.3095753
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2017
Conference article
Open Access
Cross-media learning for image sentiment analysis in the wild
Vadicamo L., Carrara F., Falchi F., Cimino A., Dell'Orletta F., Cresci S., Tesconi M.
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 investigating approaches to predict sentiments from multimedia content. With the increasing amount of data shared on social media, there is also a rapidly growing interest in approaches that work "in the wild", i.e. that are able to deal with uncontrolled conditions. In this work, we faced the challenge of training a visual sentiment classifier starting from a large set of user-generated and unlabeled contents. In particular, we collected more than 3 million tweets containing both text and images, and we leveraged on the sentiment polarity of the textual contents to train a visual sentiment classifier. To the best of our knowledge, this is the first time that a cross-media learning approach is proposed and tested in this context. We assessed the validity of our model by conducting comparative studies and evaluations on a benchmark for visual sentiment analysis. Our empirical study shows that although the text associated to each image is often noisy and weakly correlated with the image content, it can be profitably exploited to train a deep Convolutional Neural Network that effectively predicts the sentiment polarity of previously unseen images.Source: ICCV 2017 IEEE International Conference on Computer Vision Workshops, Venezia, Italy, 22-29 October 2017
DOI: 10.1109/iccvw.2017.45
Metrics:
Vadicamo L., Carrara F., Falchi F., Cimino A., Dell'Orletta F., Cresci S., Tesconi M.
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 investigating approaches to predict sentiments from multimedia content. With the increasing amount of data shared on social media, there is also a rapidly growing interest in approaches that work "in the wild", i.e. that are able to deal with uncontrolled conditions. In this work, we faced the challenge of training a visual sentiment classifier starting from a large set of user-generated and unlabeled contents. In particular, we collected more than 3 million tweets containing both text and images, and we leveraged on the sentiment polarity of the textual contents to train a visual sentiment classifier. To the best of our knowledge, this is the first time that a cross-media learning approach is proposed and tested in this context. We assessed the validity of our model by conducting comparative studies and evaluations on a benchmark for visual sentiment analysis. Our empirical study shows that although the text associated to each image is often noisy and weakly correlated with the image content, it can be profitably exploited to train a deep Convolutional Neural Network that effectively predicts the sentiment polarity of previously unseen images.Source: ICCV 2017 IEEE International Conference on Computer Vision Workshops, Venezia, Italy, 22-29 October 2017
DOI: 10.1109/iccvw.2017.45
Metrics:
See at:
ISTI Repository | arpi.unipi.it | doi.org | ieeexplore.ieee.org | CNR ExploRA