Multimodal Geolocation Estimation of News Photos

The widespread growth of multimodal news requires sophisticated approaches to interpret content and relations of different modalities. Images are of utmost importance since they represent a visual gist of the whole news article. For example, it is essential to identify the locations of natural disasters for crisis management or to analyze political or social events across the world. In some cases, verifying the location(s) claimed in a news article might help human assessors or fact-checking efforts to detect misinformation, i.e., fake news. Existing methods for geolocation estimation typically consider only a single modality, e.g., images or text. However, news images can lack sufficient geographical cues to estimate their locations, and the text can refer to various possible locations. In this paper, we propose a novel multimodal approach to predict the geolocation of news photos. To enable this approach, we introduce a novel dataset called Multimodal Geolocation Estimation of News Photos (MMG-NewsPhoto). MMG-NewsPhoto is, so far, the largest dataset for the given task and contains more than half a million news texts with the corresponding image, out of which 3000 photos were manually labeled for the photo geolocation based on information from the image-text pairs. For a fair comparison, we optimize and assess state-of-the-art methods using the new benchmark dataset. Experimental results show the superiority of the multimodal models compared to the unimodal approaches

MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. A second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalising on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding

Exploring the Geography of Tags in Youtube Views

Although tags play a critical role in many social media,their link to the geographic distribution of user generatedvideos has been little investigated. In this paper, we ana-lyze the correlation between the geographic distribution ofa video’s views and the tags attached to this video in aYoutube dataset. We show that tags can be interpreted asmarkers of a video’s geographic diffusion, with some tagsstrongly linked to well identified geographic areas. Basedon our findings, we explore whether the distribution of avideo’s views can be predicted from its tags. We demon-strate how this predictive power could help improve on-linevideo services by preferentially storing videos close to wherethey are likely to be viewed. Our results show that even witha simplistic approach we are able to predict a minimum of65.9% of a video’s views for a majority of videos, and thata tag-based placement strategy can improve the hit rate ofa distributed on-line video service by up to 6.8% globally,with an improvement of up to 34% in the USA

Learning Adaptive Representations for Image Retrieval and Recognition

Content-based image retrieval is a core problem in computer vision. It has a wide range of application such as object and place recognition, digital library search, organizing image collections, and 3D reconstruction. However, robust and accurate image retrieval from a large-scale image collection still remains an open problem. For particular instance retrieval, challenges come not only from photometric and geometric changes between the query and the database images, but also from severe visual overlap with irrelevant images. On the other hand, large intra-class variation and inter-class similarity between semantic categories represents a major obstacle in semantic image retrieval and recognition. This dissertation explores learning image representations that adaptively focus on specific image content to tackle these challenges. For this purpose, three kinds of image contexts for discriminating relevant and irrelevant image content are exploited: (1) local image context, (2) semi-global image context, and (3) global image context. Novel models for learning adaptive image representations based on each context are introduced. Moreover, as a byproduct of training the proposed models, the underlying task-relevant contexts are automatically revealed from the data in a self-supervised manner. These include data-driven notion of good local mid-level features, task-relevant semi-global contexts with rich high-level information, and the hierarchy of images. Experimental evaluation illustrates the superiority of the proposed methods in the applications of place recognition, scene categorization, and particular object retrieval.Doctor of Philosoph

Um arcabouço multimodal para geocodificação de objetos digitais

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Informação geográfica é usualmente encontrada em objetos digitais (como documentos, imagens e vídeos), sendo de grande interesse utilizá-la na implementação de diferentes serviços. Por exemplo, serviços de navegação baseados em mapas e buscas geográficas podem se beneficiar das localizações geográficas associadas a objetos digitais. A implementação destes serviços, no entanto, demanda o uso de coleções de dados geocodificados. Este trabalho estuda a combinação de conteúdo textual e visual para geocodificar objetos digitais e propõe um arcabouço de agregação de listas para geocodificação multimodal. A informação textual e visual de vídeos e imagens é usada para definir listas ordenadas. Em seguida, elas são combinadas e a nova lista ordenada resultante é usada para definir a localização geográfica de vídeos e imagens. Uma arquitetura que implementa essa proposta foi projetada de modo que módulos específicos para cada modalidade (e.g., textual ou visual) possam ser aperfeiçoados independentemente. Outro componente é o módulo de fusão responsável pela combinação das listas ordenadas definidas por cada modalidade. Outra contribuição deste trabalho é a proposta de uma nova medida de avaliação da efetividade de métodos de geocodificação chamada Weighted Average Score (WAS). Ela é baseada em ponderações de distâncias que permitem avaliar a efetividade de uma abordagem, considerando todos os resultados de geocodificação das amostras de teste. O arcabouço proposto foi validado em dois contextos: desafio Placing Task da iniciativa MediaEval 2012, que consiste em atribuir, automaticamente, coordenadas geográficas a vídeos; e geocodificação de fotos de prédios da Virginia Tech (VT) nos EUA. No contexto do desafio Placing Task, os resultados mostram como nossa abordagem melhora a geocodificação em comparação a métodos que apenas contam com uma modalidade (sejam descritores textuais ou visuais). Nós mostramos ainda que a proposta multimodal produziu resultados comparáveis às melhores submissões que também não usavam informações adicionais além daquelas disponibilizadas na base de treinamento. Em relação à geocodificação das fotos de prédios da VT, os experimentos demostraram que alguns dos descritores visuais locais produziram resultados efetivos. A seleção desses descritores e sua combinação melhoraram esses resultados quando a base de conhecimento tinha as mesmas características da base de testeAbstract: Geographical information is often enclosed in digital objects (like documents, images, and videos) and its use to support the implementation of different services is of great interest. For example, the implementation of map-based browser services and geographic searches may take advantage of geographic locations associated with digital objects. The implementation of such services, however, demands the use of geocoded data collections. This work investigates the combination of textual and visual content to geocode digital objects and proposes a rank aggregation framework for multimodal geocoding. Textual and visual information associated with videos and images are used to define ranked lists. These lists are later combined, and the new resulting ranked list is used to define appropriate locations. An architecture that implements the proposed framework is designed in such a way that specific modules for each modality (e.g., textual and visual) can be developed and evolved independently. Another component is a data fusion module responsible for combining seamlessly the ranked lists defined for each modality. Another contribution of this work is related to the proposal of a new effectiveness evaluation measure named Weighted Average Score (WAS). The proposed measure is based on distance scores that are combined to assess how effective a designed/tested approach is, considering its overall geocoding results for a given test dataset. We validate the proposed framework in two contexts: the MediaEval 2012 Placing Task, whose objective is to automatically assign geographical coordinates to videos; and the task of geocoding photos of buildings from Virginia Tech (VT), USA. In the context of Placing Task, obtained results show how our multimodal approach improves the geocoding results when compared to methods that rely on a single modality (either textual or visual descriptors). We also show that the proposed multimodal approach yields comparable results to the best submissions to the Placing Task in 2012 using no additional information besides the available development/training data. In the context of the task of geocoding VT building photos, performed experiments demonstrate that some of the evaluated local descriptors yield effective results. The descriptor selection criteria and their combination improved the results when the used knowledge base has the same characteristics of the test setDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Exploring the Geography of Tags in Youtube Views

Although tags play a critical role in many social media,their link to the geographic distribution of user generatedvideos has been little investigated. In this paper, we ana-lyze the correlation between the geographic distribution ofa video’s views and the tags attached to this video in aYoutube dataset. We show that tags can be interpreted asmarkers of a video’s geographic diffusion, with some tagsstrongly linked to well identified geographic areas. Basedon our findings, we explore whether the distribution of avideo’s views can be predicted from its tags. We demon-strate how this predictive power could help improve on-linevideo services by preferentially storing videos close to wherethey are likely to be viewed. Our results show that even witha simplistic approach we are able to predict a minimum of65.9% of a video’s views for a majority of videos, and thata tag-based placement strategy can improve the hit rate ofa distributed on-line video service by up to 6.8% globally,with an improvement of up to 34% in the USA