Abstract The rapid spread of misinformation poses a significant challenge in the digital age, with false claims appearing across multiple modalities, particularly combinations of textual and visual content such as images and videos. While automatic fact-checking plays a crucial role in countering misinformation, traditional approaches predominantly rely on textual data, often neglecting the multimodal nature of modern misinformation. In this survey, we provide a comprehensive evaluation of multimodal datasets designed for automatic fact-checking that combine textual and visual information, systematically analyzing their sources, annotation methodologies, and key statistical properties, such as class distribution, topic diversity, and label availability. Additionally, we assess the usability of these datasets in real-world scenarios, discussing their limitations, biases, and potential risks, such as information leakage. Motivated by the practical difficulties we encountered when attempting to integrate existing datasets into our own multimodal fact-checking pipeline, our work also offers concrete guidance to help researchers choose the most suitable resources. By identifying gaps and challenges in existing datasets, our survey aims to support the development of more reliable and scalable multimodal fact-checking systems.

Metaverse environments offer immersive, multimedia-rich experiences with growing relevance in education, entertainment, and cultural applications. The ability to grasp the contents of these environments, consisting of many rooms or subspaces, is a key building block for implementing Metaverse retrieval systems. However, current methods remain limited, as they are not designed to separate local (e.g., individual multimedia elements, and room-level details) from global, Metaverse-level semantics. Moreover, public datasets on similar topics do not capture the complexity of multi-room environments filled with multimedia contents. Our contributions are twofold. First, we introduce HiCALM, a hierarchical Metaverse retrieval framework built around the structural hierarchy of Metaverse environments. HiCALM models Metaverses in a bottom-up way, progressively grasping local and global semantics. To reduce the gap with textual data and achieve high-performance text-to-Metaverse retrieval, a novel cross-modal hierarchical loss supervises the process by teaching the model to associate the hierarchical visual features with textual information, also extracted hierarchically. Second, to overcome the absence of suitable datasets for the task, we present Museums3k, a large-scale dataset of 3,000 virtual museums annotated with detailed descriptions, each composed of multiple rooms populated with diverse multimedia elements; and GamingMV, a smaller dataset with data coming from 239 gaming-related real-world Metaverses. Through extensive quantitative and qualitative experiments, we show that HiCALM achieves considerable improvements in text-to-Metaverse retrieval, obtaining up to 95.0% R@1 and 60.0% \(\text{nDCG}_{3}@10\) on Museums3k (more than +40% R@1 and +11% nDCG, compared to existing solutions), and up to 62.0% R@1 and 62.6% \(\text{nDCG}_{3}@10\) on GamingMV (more than +23% R@1 and +5% nDCG).

This reproducibility Companion paper supports the approach presented in our paper titled ''AdOCTeRA: Adaptive Optimization Constraints for Improved Text-Guided Retrieval of Apartments.'' In that work, we addressed the problem of apartment retrieval using textual descriptions. Specifically, we proposed a novel adaptive approach that leverages the similarity between apartment descriptions in the dataset to enforce different levels of distance-a small distance between similar elements, a slightly larger distance between less similar elements, and an even larger distance between dissimilar elements.

The Metaverse and its immersive environments are gaining significant attention due to their potential applications across various fields, from healthcare to art. As their numbers grow, it becomes difficult to effectively search through them and identify those of interest to the user. Recently, Metaverses were modeled as multimedia-rich 3D scenarios. However, existing works on retrieving them via text have several shortcomings, including the lack of experimentation with joint analysis of heterogeneous multimedia formats within the Metaverse, the use of small-scale datasets with randomly aggregated elements, and the consequent lack of thematic coherence in retrieval methods. To address these issues, we introduce SAVAGE, a novel synthetic dataset of 10,000 thematic exhibitions containing both real-world paintings and generated video artworks. Moreover, we propose HM3, a new hierarchical methodology for Metaverse Retrieval which captures all the contents of the room and integrates both images and videos, while its training is guided by a novel theme-aware loss function. Experiments on SAVAGE demonstrate the effectiveness of HM3 in modelling museums. The method also shows considerable improvements on an existing dataset of Metaverses, with ablation studies and qualitative analyses confirming the utility of the proposed theme-aware loss function.

The Metaverse is growing rapidly, resulting in thousands of rich virtual universes. This results in a difficult search process for the user, making advanced search tools a necessity. Existing methods leverage contrastive learning to obtain a function mapping a 3D scene and its textual descriptions into similar representations. However, Metaverse scenarios are complex, multimedia-rich 3D scenes containing many elements, making cross-modal alignment difficult. For instance, a museum dedicated to Van Gogh is unrelated to Warhol, yet it shares similarities with Matisse or Monet. To make the mapping functions aware of these nuances, we propose a novel learning strategy to integrate Adaptive Optimization Constraints, computing data-dependent distances using a language-based method we design and enforcing them between the representations at training time. This novelty sets our approach apart from standard procedures enforcing the same distance. We validate the effectiveness of two datasets, one including 6000 apartments, and a novel dataset of 3000 museums that we collect. We observe consistent improvements compared to existing methods. Moreover, we obtain better generalization when with very complex scenarios, e.g. on the museums dataset it obtains an average R@1 of 5.2% compared to 1.2% obtained by existing methods. Finally, the source code is available athttps://github.com/aliabdari/ALCER3D.

Nowadays, it is common for workers to relocate to new countries while seeking better job opportunities, or to live as digital nomads. While doing so, they face the problem of finding a new place to call home, requiring them to trust online advertisements or to physically visit the apartment. Recently, the research community investigated the possibility of performing the search on the Metaverse, hence reducing time and costs related to traveling and limiting carbon emissions. The methods available are based on state-of-the-art cross-modal retrieval techniques, which learn a joint embedding space by mapping apartment-descriptions pairs close. However, these methodologies push all the other pairs far away in the embedding space. In this paper, we identify this decision as a limitation, since different apartments are likely to share many aspects. To overcome it, we propose AdOCTeRA, which automatically separates the apartments into three classes -- very similar, slightly similar, and dissimilar -- and proposes adaptive optimization constraints for each of them. We validate our methodology on a large dataset of more than 6000 apartments, obtaining considerable relative improvements over the previous state-of-the-art (+3.8% R@5 and +7.3% R@10), and consistent improvements over the baseline across all the experiments. The source code is available at \hrefhttps://github.com/aliabdari/AdOCTeRA https://github.com/aliabdari/AdOCTeRA

To retrieve a video via a multimedia search engine, a textual query is usually created by the user and then used to perform the search. Recent state-of-the-art cross-modal retrieval methods learn a joint text-video embedding space by using contrastive loss functions, which maximize the similarity of positive pairs while decreasing that of the negative pairs. Although the choice of these pairs is fundamental for the construction of the joint embedding space, the selection procedure is usually driven by the relationships found within the dataset: a positive pair is commonly formed by a video and its own caption, whereas unrelated video-caption pairs represent the negative ones. We hypothesize that this choice results in a retrieval system with limited semantics understanding, as the standard training procedure requires the system to discriminate between groundtruth and negative even though there is no difference in their semantics. Therefore, differently from the previous approaches, in this paper we propose a novel strategy for the selection of both positive and negative pairs which takes into account both the annotations and the semantic contents of the captions. By doing so, the selected negatives do not share semantic concepts with the positive pair anymore, and it is also possible to discover new positives within the dataset. Based on our hypothesis, we provide a novel design of two popular contrastive loss functions, and explore their effectiveness on three heterogeneous state-of-the-art approaches. The extensive experimental analysis conducted on two datasets, EPIC-Kitchens-100 and MSR-VTT, validates the effectiveness of the proposed strategy, observing, e.g., more than +20% nDCG on EPIC-Kitchens-100. Furthermore, these results are corroborated with qualitative evidence both supporting our hypothesis and explaining why the proposed strategy effectively overcomes it.

Multivariate time series classification is an ubiquitous and widely studied problem. Due to their strong generalization capability, neural networks are suitable for this problem, but their intrinsic black-box nature often limits their applicability. Temporal decision trees are a relevant alternative to neural networks for the same task regarding classification performances while attaining higher levels of transparency and interpretability. In this work, we approach the problem of hybridizing these two techniques, and present three independent, natural hybridization solutions to study if, and in what measure, both the ability of neural networks to capture complex temporal patterns and the transparency and flexibility of temporal decision trees can be leveraged. To this end, we provide initial experimental results for several tasks in a binary classification setting, showing that our proposed neural-symbolic hybridization schemata may be a step towards accurate and interpretable models.

Adverse Drug Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Language Processing community using large pre-trained language models, such as BERT. Despite the great number of Transformer-based architectures used in the literature, it is unclear which of them has better performances and why. Therefore, in this paper we perform an extensive evaluation and analysis of 19 Transformer-based models for ADE extraction on informal texts. We compare the performance of all the considered models on two datasets with increasing levels of informality (forums posts and tweets). We also combine the purely Transformer-based models with two commonly-used additional processing layers (CRF and LSTM), and analyze their effect on the models performance. Furthermore, we use a well-established feature importance technique (SHAP) to correlate the performance of the models with a set of features that describe them: model category (AutoEncoding, AutoRegressive, Text-to-Text), pre-training domain, training from scratch, and model size in number of parameters. At the end of our analyses, we identify a list of take-home messages that can be derived from the experimental data.

Abstract Video Question Answering (VideoQA) concerns the realization of models able to analyze a video, and produce a meaningful answer to visual content-related questions. To encode the given question, word embedding techniques are used to compute a representation of the tokens suitable for neural networks. Yet almost all the works in the literature use the same technique, although recent advancements in NLP brought better solutions. This lack of analysis is a major shortcoming. To address it, in this paper we present a twofold contribution about this inquiry and its relation with question encoding. First of all, we integrate four of the most popular word embedding techniques in three recent VideoQA architectures, and investigate how they influence the performance on two public datasets: EgoVQA and PororoQA. Thanks to the learning process, we show that embeddings carry question type-dependent characteristics. Secondly, to leverage this result, we propose a simple yet effective multi-task learning protocol which uses an auxiliary task defined on the question types. By using the proposed learning strategy, significant improvements are observed in most of the combinations of network architecture and embedding under analysis.

Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of non-expert is exploited. We study whether crowdsourcing is an effective and reliable method to assess truthfulness during a pandemic, targeting statements related to COVID-19, thus addressing (mis)information that is both related to a sensitive and personal issue and very recent as compared to when the judgment is done. In our experiments, crowd workers are asked to assess the truthfulness of statements, and to provide evidence for the assessments. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we report results on workers' behavior, agreement among workers, effect of aggregation functions, of scales transformations, and of workers background and bias. We perform a longitudinal study by re-launching the task multiple times with both novice and experienced workers, deriving important insights on how the behavior and quality change over time. Our results show that workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e.g., source of information, behavior) impact the quality of the data. The longitudinal study demonstrates that the time-span has a major effect on the quality of the judgments, for both novice and experienced workers. Finally, we provide an extensive failure analysis of the statements misjudged by the crowd-workers.

Medical term normalization consists in mapping a piece of text to a large number of output classes.Given the small size of the annotated datasets and the extremely long tail distribution of the concepts, it is of utmost importance to develop models that are capable to generalize to scarce or unseen concepts.An important attribute of most target ontologies is their hierarchical structure.In this paper we introduce a simple and effective learning strategy that leverages such information to enhance the generalizability of both discriminative and generative models.The evaluation shows that the proposed strategy produces state-of-the-art performance on seen concepts and consistent improvements on unseen ones, allowing also for efficient zero-shot knowledge transfer across text typologies and datasets.

Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \urlhttps://github.com/aranciokov/RelevanceMargin-ICMR22.

Every hour, huge amounts of visual contents are posted on social media and user-generated content platforms. To find relevant videos by means of a natural language query, text-video retrieval methods have received increased attention over the past few years. Data augmentation techniques were introduced to increase the performance on unseen test examples by creating new training samples with the application of semantics-preserving techniques, such as color space or geometric transformations on images. Yet, these techniques are usually applied on raw data, leading to more resource-demanding solutions and also requiring the shareability of the raw data, which may not always be true, e.g. copyright issues with clips from movies or TV series. To address this shortcoming, we propose a multimodal data augmentation technique which works in the feature space and creates new videos and captions by mixing semantically similar samples. We experiment our solution on a large scale public dataset, EPIC-Kitchens-100, and achieve considerable improvements over a baseline method, improved state-of-the-art performance, while at the same time performing multiple ablation studies. We release code and pretrained models on Github at https://github.com/aranciokov/FSMMDA\_VideoRetrieval.

Beatrice Portelli, Edoardo Lenzi, Emmanuele Chersoni, Giuseppe Serra, Enrico Santus. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large. Very recently, the problem has been addressed with a crowdsourcing-based approach to scale up labeling efforts: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of (non-expert) judges is exploited. We follow the same approach to study whether crowdsourcing is an effective and reliable method to assess statements truthfulness during a pandemic. We specifically target statements related to the COVID-19 health emergency, that is still ongoing at the time of the study and has arguably caused an increase of the amount of misinformation that is spreading online (a phenomenon for which the term "infodemic" has been used). By doing so, we are able to address (mis)information that is both related to a sensitive and personal issue like health and very recent as compared to when the judgment is done: two issues that have not been analyzed in related work.\n\nIn our experiment, crowd workers are asked to assess the truthfulness of statements, as well as to provide evidence for the assessments as a URL and a text justification. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we also report results on many different aspects, including: agreement among workers, the effect of different aggregation functions, of scales transformations, and of workers background / bias. We also analyze workers behavior, in terms of queries submitted, URLs found / selected, text justifications, and other behavioral data like clicks and mouse actions collected by means of an ad hoc logger.

MOTIVATION: Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. RESULTS: GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of 'perfect' GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with 'perfect' GB radii of 0.97 and ca. 20% smaller root mean square error. AVAILABILITY AND IMPLEMENTATION: We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations. In this paper we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a Convolutional LSTM that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. Additionally, to tackle the center bias typical of human eye fixations, our model can learn a set of prior maps generated with Gaussian functions. We show, through an extensive evaluation, that the proposed architecture outperforms the current state of the art on public saliency prediction datasets. We further study the contribution of each key component to demonstrate their robustness on different scenarios.

Image captioning has been recently gaining a lot of attention thanks to the impressive achievements shown by deep captioning architectures, which combine Convolutional Neural Networks to extract image representations and Recurrent Neural Networks to generate the corresponding captions. At the same time, a significant research effort has been dedicated to the development of saliency prediction models, which can predict human eye fixations. Even though saliency information could be useful to condition an image captioning architecture, by providing an indication of what is salient and what is not, research is still struggling to incorporate these two techniques. In this work, we propose an image captioning approach in which a generative recurrent neural network can focus on different parts of the input image during the generation of the caption, by exploiting the conditioning given by a saliency prediction model on which parts of the image are salient and which are contextual. We show, through extensive quantitative and qualitative experiments on large-scale datasets, that our model achieves superior performance with respect to captioning baselines with and without saliency and to different state-of-the-art approaches combining saliency and captioning.

The papers in this special section focus on the deployment of wearable computing technologies and ego-vision systems for augmented reality applications. Rapid progress in the development of low-level component technologies such as wearable sensors, wearable displays, and wearable computers is making our digital lives grow, connect, and play a relevant role in reality. To name a few examples, body-mounted sensors and displays help athletes in training by presenting real-time performance metrics such as speed, distance, and heart rate.Wearable systems allow medical staff in hospitals to consult specialists located anywhere in the world, in real time, providing optimal patient care. And within the context of assistive technologies, a head-mounted camera can be used to identify and convey the presence of objects, people, or text to a visually impaired user.

In this paper, we propose a new method for customized summarization of egocentric videos according to specific user preferences, so that different users can extract different summaries from the same stream. Our approach, tailored on a cultural heritage scenario, relies on creating a short synopsis of the original video focused on key shots, in which concepts relevant to user preferences can be visually detected and the chronological flow of the original video is preserved. Moreover, we release a new dataset, composed of egocentric streams taken in uncontrolled scenarios, capturing tourists cultural visits in six art cities, with geolocalization information. Our experimental results show that the proposed approach is able to leverage user's preferences with an accent on storyline chronological flow and on visual smoothness.

This paper presents a novel deep architecture for saliency prediction. Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps. We propose an architecture which, instead, combines features extracted at different levels of a Convolutional Neural Network (CNN). Our model is composed of three main blocks: a feature extraction CNN, a feature encoding network, that weights low and high level feature maps, and a prior learning network. We compare our solution with state of the art saliency models on two public benchmarks datasets. Results show that our model outperforms under all evaluation metrics on the SALICON dataset, which is currently the largest public dataset for saliency prediction, and achieves competitive results on the MIT300 benchmark. Code is available at https://github.com/marcellacornia/mlnet.

Nowadays, egocentric wearable devices are continuously increasing their widespread among both the academic community and the general public. For this reason, methods capable of automatically segment the video based on the recorder motion patterns are gaining attention. These devices present the unique opportunity of both high quality video recordings and multimodal sensors readings. Significant efforts have been made in either analyzing the video stream recorded by these devices or the bio-mechanical sensor information. So far, the integration between these two realities has not been fully addressed, and the real capabilities of these devices are not yet exploited. In this paper, we present a solution to segment a video sequence into motion activities by introducing a novel data fusion technique based on the covariance of visual and bio-mechanical features. The experimental results are promising and show that the proposed integration strategy outperforms the results achieved focusing solely on a single source.

The new technologies characterizing the Internet of Things (IoT) allow realizing real smart environments able to provide advanced services to the users. Recently, these smart environments are also being exploited to renovate the users' interest on the cultural heritage, by guaranteeing real interactive cultural experiences. In this paper, we design and validate an indoor location-aware architecture able to enhance the user experience in a museum. In particular, the proposed system relies on a wearable device that combines image recognition and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. The localization information is obtained by a Bluetooth low energy (BLE) infrastructure installed in the museum. Moreover, the system interacts with the Cloud to store multimedia contents produced by the user and to share environment-generated events on his/her social networks. Finally, several location-aware services, running in the system, control the environment status also according to users' movements. These services interact with physical devices through a multiprotocol middleware. The system has been designed to be easily extensible to other IoT technologies and its effectiveness has been evaluated in the MUST museum, Lecce, Italy.

Recent egocentric video summarization approaches have dealt with motion analysis and social interaction without considering that user can be interested in preserving only part of the video related to his interests. In this paper we propose a new method for personalized video summarization of cultural experiences with the goal of extracting from the streams only the scenes corresponding to a user's specific topics request, chosen among the shots in which it's possible to deduce that the visitor was focusing on a point of interest. Preliminary experiments show that our approach is promising and allows visitor to better customize the summary of his experience.

In this paper, we propose a new method to obtain customized video summarization according to specific user preferences. Our approach is tailored on Cultural Heritage scenario and is designed on identifying candidate shots, selecting from the original streams only the scenes with behavior patterns related to the presence of relevant experiences, and further filtering them in order to obtain a summary matching the requested user's preferences. Our preliminary results show that the proposed approach is able to leverage user's preferences in order to obtain a customized summary, so that different users may extract from the same stream different summaries.

This paper reports a novel approach to deal with the problem of Object and Scene recognition extending the traditional Bag of Words approach in two ways. Firstly, a dataset independent method of summarizing local features, based on multivariate Gaussian descriptors, is employed. Secondly, a recently proposed classification technique, particularly suited for high dimensional feature spaces without any dimensionality reduction step, allows to effectively exploit these features. Experiments are performed on two publicly available datasets and demonstrate the effectiveness of our approach when compared to state-of-the-art methods.

In this paper we present a new method for head pose real-time estimation in ego-vision scenarios that is a key step in the understanding of social interactions. In order to robustly detect head under changing aspect ratio, scale and orientation we use and extend the Hough-Based Tracker which allows to follow simultaneously each subject in the scene. In an ego-vision scenario where a group interacts in a discussion, each subject's head orientation will be more likely to remain focused for a while on the person who has the floor. In order to encode this behavior we include a stateful Hidden Markov Model technique that enforces the predicted pose with the temporal coherence from a video sequence. We extensively test our approach on several indoor and outdoor ego-vision videos with high illumination variations showing its validity and outperforming other recent related state of the art approaches.

In this paper we propose a novel image descriptor built by computing the covariance of pixel level features on densely sampled patches and encoding them using their covariance. Appropriate projections to the Euclidean space and feature normalizations are employed in order to provide a strong descriptor usable with linear classifiers. In order to remove border effects, we further enhance the Spatial Pyramid representation with bilinear interpolation. Experimental results conducted on two common datasets for object and texture classification show that the performance of our method is comparable with state of the art techniques, but removing any dataset specific dependency in the feature encoding step.

In this paper, we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one's bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement. We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines.

Common techniques represent images by quantizing local descriptors and summarizing their distribution in a histogram. In this paper we propose to employ a parametric description and compare its capabilities to histogram based approaches. We use the multivariate Gaussian distribution, applied over the SIFT descriptors, extracted with dense sampling on a spatial pyramid. Every distribution is converted to a high-dimensional descriptor, by concatenating the mean vector and the projection of the covariance matrix on the Euclidean space tangent to the Riemannian manifold. Experiments on Caltech-101 and ImageCLEF2011 are performed using the Stochastic Gradient Descent solver, which allows to deal with large scale datasets and high dimensional feature spaces.

We contribute, through this paper, to the design of a novel variational framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos and test images are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing: 1) a fidelity term that measures the quality of feature matching, 2) a neighborhood criterion that captures feature co-occurrence/geometry, and 3) a regularization term that controls the smoothness of the matching solution. We also introduce a detection/recognition procedure and study its theoretical consistency. Finally, we show the validity of our method through extensive experiments on the challenging MICC-Logos dataset. Our method overtakes, by 20%, baseline as well as state-of-the-art matching/recognition procedures.

Recognition and classification of human actions for annotation of unconstrained video sequences has proven to be challenging because of the variations in the environment, appearance of actors, modalities in which the same action is performed by different persons, speed and duration, and points of view from which the event is observed. This variability reflects in the difficulty of defining effective descriptors and deriving appropriate and effective codebooks for action categorization. In this paper, we propose a novel and effective solution to classify human actions in unconstrained videos. It improves on previous contributions through the definition of a novel local descriptor that uses image gradient and optic flow to respectively model the appearance and motion of human actions at interest point regions. In the formation of the codebook, we employ radius-based clustering with soft assignment in order to create a rich vocabulary that may account for the high variability of human actions. We show that our solution scores very good performance with no need of parameter tuning. We also show that a strong reduction of computation time can be obtained by applying codebook size reduction with Deep Belief Networks with little loss of accuracy.

Tagging of multimedia content is becoming more and more widespread as web 2.0 sites, like Flickr and Facebook for images, YouTube and Vimeo for videos, have popularized tagging functionalities among their users. These user-generated tags are used to retrieve multimedia content, and to ease browsing and exploration of media collections, e.g.~using tag clouds. However, not all media are equally tagged by users: using the current browsers is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook; on the other hand tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a system for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to shots. This approach exploits collective knowledge embedded in tags and Wikipedia, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr.

One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.

In many application scenarios digital images play a basic role and often it is important to assess if their content is realistic or has been manipulated to mislead watcher's opinion. Image forensics tools provide answers to similar questions. This paper, in particular, focuses on the problem of detecting if a feigned image has been created by cloning an area of the image onto another zone to make a duplication or to cancel something awkward. The proposed method is based on SIFT features and allows both to understand which are the image points involved in the counterfeit attack and, furthermore, to recover the parameters of the geometric transformation. Experimental results are provided to witness the powerfulness of the proposed technique.

In this technical demonstration we show an integrated web system for video search and annotation based on ontologies. The system is composed by three components: the Orione ontology-based search engine, the Sirio\footnote{Sirio was the hound of Orione. It was a dog so swift that no prey could escape it.} search interface, and the Pan web-based video annotation tool. The system is currently being developed within the EU IM3I project. The goal of the system is to provide an integrated environment for video annotation and retrieval of videos, for both technical and non-technical users. In fact, the search engine has different interfaces that permit different query modalities: free-text, natural language, graphical composition of concepts using Boolean and temporal relations and query by visual example. In addition, the ontology structure is exploited to encode semantic relations between concepts permitting, for example, to expand queries to synonyms and concept specializations. The annotation tool can be used to create ground-truth annotations to train automatic annotations systems, or to complement the results of automatic annotation, e.g. adding geolocalized information.

In this technical demonstration we present a novel web-based tool that allows a user friendly semantic browsing of video collections, based on ontologies, concepts, concept relations and concept clouds. The system is developed as a Rich Internet Application (RIA) to achieve a fast responsiveness and ease of use that can not be obtained by other web application paradigms, and uses streaming to access and inspect the videos. Users can also use the tool to browse the content of social and media sharing sites like YouTube, Flickr and Twitter, accessing these external resources through the ontologies used in the system. The tool has won the second prize in the Adobe YouGC contest, in the RIA category.

In this paper we propose a new method for human action categorization by using an effective combination of a new 3D gradient descriptor with an optic flow descriptor, to represent spatio-temporal interest points. These points are used to represent video sequences using a bag of spatio-temporal visual words, following the successful results achieved in object and scene classification. We extensively test our approach on the standard KTH and Weizmann actions datasets, showing its validity and good performance. Experimental results outperform state-of-the-art methods, without requiring fine parameter tuning.

In this work is presented a novel approach for the classification of audio concepts in broadcast soccer videos using deep belief network (DBN), a probabilistic neural network with several hidden layers. Comparison with support vector machine (SVM) classifiers has been carried on, showing that our preliminary results are promisingly comparable to the state-of-the-art.

In this technical demonstration we show the current version of Arneb, a web-based system for manual annotation of videos, developed within the EU VidiVideo project. This tool has been developed with the aim of creating ground truth annotations, that can be used for training and evaluating automatic video annotation systems. Annotations can be exported to MPEG-7 and OWL ontologies. The system has been developed according to the Rich Internet Application paradigm, allowing collaborative web-based annotation.

In this technical demonstration we show a web video search engine based on ontologies, the Sirio system, that has been developed within the EU VidiVideo project. The goal of the system is to provide a search engine for videos for both technical and non-technical users. In fact, the system has different interfaces that permit different query modalities: free-text, natural language, graphical composition of concepts using boolean and temporal relations and query by visual example. In addition, the ontology structure is exploited to encode semantic relations between concepts permitting, for example, to expand queries to synonyms and concept specializations.

A calibration algorithm of two cameras using observations of a moving person is presented. Similar methods have been proposed for self-calibration with a single camera, but internal parameter estimation is only limited to the focal length. Recently it has been demonstrated that principal point supposed in the center of the image causes inaccuracy of all estimated parameters. Our method exploits two cameras, using image points of head and foot locations of a moving person, to determine for both cameras the focal length and the principal point. Moreover with the increasing number of cameras there is a demand of procedures to determine their relative placements. In this paper we also describe a method to find the relative position and orientation of two cameras: the rotation matrix and the translation vector which describe the rigid motion between the coordinate frames fixed in two cameras. Results in synthetic and real scenes are presented to evaluate the performance of the proposed method.