Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semi-supervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) i2015FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP [2013/08645-0, 2013/50169-1]CNPq [306580/2012-8, 484254/2012-0]2013/08645-0; 2013/50169-1306580/2012-8;484254/2012-0SEM INFORMAÇÃ

Toward Entity-Aware Search

As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability

Agregação de ranks baseada em grafos

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Neste trabalho, apresentamos uma abordagem robusta de agregação de listas baseada em grafos, capaz de combinar resultados de modelos de recuperação isolados. O método segue um esquema não supervisionado, que é independente de como as listas isoladas são geradas. Nossa abordagem é capaz de incorporar modelos heterogêneos, de diferentes critérios de recuperação, tal como baseados em conteúdo textual, de imagem ou híbridos. Reformulamos o problema de recuperação ad-hoc como uma recuperação baseada em fusion graphs, que propomos como um novo modelo de representação unificada capaz de mesclar várias listas e expressar automaticamente inter-relações de resultados de recuperação. Assim, mostramos que o sistema de recuperação se beneficia do aprendizado da estrutura intrínseca das coleções, levando a melhores resultados de busca. Nossa formulação de agregação baseada em grafos, diferentemente das abordagens existentes, permite encapsular informação contextual oriunda de múltiplas listas, que podem ser usadas diretamente para ranqueamento. Experimentos realizados demonstram que o método apresenta alto desempenho, produzindo melhores eficácias que métodos recentes da literatura e promovendo ganhos expressivos sobre os métodos de recuperação fundidos. Outra contribuição é a extensão da proposta de grafo de fusão visando consulta eficiente. Trabalhos anteriores são promissores quanto à eficácia, mas geralmente ignoram questões de eficiência. Propomos uma função inovadora de agregação de consulta, não supervisionada, intrinsecamente multimodal almejando recuperação eficiente e eficaz. Introduzimos os conceitos de projeção e indexação de modelos de representação de agregação de consulta com base em grafos, e a sua aplicação em tarefas de busca. Formulações de projeção são propostas para representações de consulta baseadas em grafos. Introduzimos os fusion vectors, uma representação de fusão tardia de objetos com base em listas, a partir da qual é definido um modelo de recuperação baseado intrinsecamente em agregação. A seguir, apresentamos uma abordagem para consulta rápida baseada nos vetores de fusão, promovendo agregação de consultas eficiente. O método apresentou alta eficácia quanto ao estado da arte, além de trazer uma perspectiva de eficiência pouco abordada. Ganhos consistentes de eficiência são alcançadas em relação aos trabalhos recentes. Também propomos modelos de representação baseados em consulta para problemas gerais de predição. Os conceitos de grafos de fusão e vetores de fusão são estendidos para cenários de predição, nos quais podem ser usados para construir um modelo de estimador para determinar se um objeto de avaliação (ainda que multimodal) se refere a uma classe ou não. Experimentos em tarefas de classificação multimodal, tal como detecção de inundação, mostraram que a solução é altamente eficaz para diferentes cenários de predição que envolvam dados textuais, visuais e multimodais, produzindo resultados melhores que vários métodos recentes. Por fim, investigamos a adoção de abordagens de aprendizagem para ajudar a otimizar a criação de modelos de representação baseados em consultas, a fim de maximizar seus aspectos de capacidade discriminativa e eficiência em tarefas de predição e de buscaAbstract: In this work, we introduce a robust graph-based rank aggregation approach, capable of combining results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to incorporate heterogeneous models, defined in terms of different ranking criteria, such as those based on textual, image, or hybrid content representations. We reformulate the ad-hoc retrieval problem as a graph-based retrieval based on {\em fusion graphs}, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we show that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused. Another contribution refers to the extension of the fusion graph solution for efficient rank aggregation. Although previous works are promising with respect to effectiveness, they usually overlook efficiency aspects. We propose an innovative rank aggregation function that it is unsupervised, intrinsically multimodal, and targeted for fast retrieval and top effectiveness performance. We introduce the concepts of embedding and indexing graph-based rank-aggregation representation models, and their application for search tasks. Embedding formulations are also proposed for graph-based rank representations. We introduce the concept of {\em fusion vectors}, a late-fusion representation of objects based on ranks, from which an intrinsically rank-aggregation retrieval model is defined. Next, we present an approach for fast retrieval based on fusion vectors, thus promoting an efficient rank aggregation system. Our method presents top effectiveness performance among state-of-the-art related work, while promoting an efficiency perspective not yet covered. Consistent speedups are achieved against the recent baselines in all datasets considered. Derived from the fusion graphs and fusion vectors, we propose rank-based representation models for general prediction problems. The concepts of fusion graphs and fusion vectors are extended to prediction scenarios, where they can be used to build an estimator model to determine whether an input (even multimodal) object refers to a class or not. Performed experiments in the context of multimodal classification tasks, such as flood detection, show that the proposed solution is highly effective for different detection scenarios involving textual, visual, and multimodal features, yielding better detection results than several state-of-the-art methods. Finally, we investigate the adoption of learning approaches to help optimize the creation of rank-based representation models, in order to maximize their discriminative power and efficiency aspects in prediction and search tasksDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval

The knowledge representation community has built general-purpose ontologies which contain large amounts of commonsense knowledge over relevant aspects of the world, including useful visual information, e.g.: "a ball is used by a football player", "a tennis player is located at a tennis court". Current state-of-the-art approaches for visual recognition do not exploit these rule-based knowledge sources. Instead, they learn recognition models directly from training examples. In this paper, we study how general-purpose ontologies---specifically, MIT's ConceptNet ontology---can improve the performance of state-of-the-art vision systems. As a testbed, we tackle the problem of sentence-based image retrieval. Our retrieval approach incorporates knowledge from ConceptNet on top of a large pool of object detectors derived from a deep learning technique. In our experiments, we show that ConceptNet can improve performance on a common benchmark dataset. Key to our performance is the use of the ESPGAME dataset to select visually relevant relations from ConceptNet. Consequently, a main conclusion of this work is that general-purpose commonsense ontologies improve performance on visual reasoning tasks when properly filtered to select meaningful visual relations.Comment: Accepted in IJCAI-1

Learning Task Relatedness in Multi-Task Learning for Images in Context

Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property. When task relations are explicitly defined based on domain knowledge multi-task learning (MTL) offers such concurrent solutions, while exploiting relatedness between multiple tasks performed over the same dataset. In most cases however, this relatedness is not explicitly defined and the domain expert knowledge that defines it is not available. To address this issue, we introduce Selective Sharing, a method that learns the inter-task relatedness from secondary latent features while the model trains. Using this insight, we can automatically group tasks and allow them to share knowledge in a mutually beneficial way. We support our method with experiments on 5 datasets in classification, regression, and ranking tasks and compare to strong baselines and state-of-the-art approaches showing a consistent improvement in terms of accuracy and parameter counts. In addition, we perform an activation region analysis showing how Selective Sharing affects the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster