6,536 research outputs found

    Limitations of Cross-Lingual Learning from Image Search

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    Cross-lingual representation learning is an important step in making NLP scale to all the world's languages. Recent work on bilingual lexicon induction suggests that it is possible to learn cross-lingual representations of words based on similarities between images associated with these words. However, that work focused on the translation of selected nouns only. In our work, we investigate whether the meaning of other parts-of-speech, in particular adjectives and verbs, can be learned in the same way. We also experiment with combining the representations learned from visual data with embeddings learned from textual data. Our experiments across five language pairs indicate that previous work does not scale to the problem of learning cross-lingual representations beyond simple nouns

    Visualising the structure of document search results: A comparison of graph theoretic approaches

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    This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion

    The TREC-2002 video track report

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    TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings

    Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

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    We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure

    iQPP: A Benchmark for Image Query Performance Prediction

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    To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image. To boost the exploration of the QPP task in image retrieval, we propose the first benchmark for image query performance prediction (iQPP). First, we establish a set of four data sets (PASCAL VOC 2012, Caltech-101, ROxford5k and RParis6k) and estimate the ground-truth difficulty of each query as the average precision or the precision@k, using two state-of-the-art image retrieval models. Next, we propose and evaluate novel pre-retrieval and post-retrieval query performance predictors, comparing them with existing or adapted (from text to image) predictors. The empirical results show that most predictors do not generalize across evaluation scenarios. Our comprehensive experiments indicate that iQPP is a challenging benchmark, revealing an important research gap that needs to be addressed in future work. We release our code and data as open source at https://github.com/Eduard6421/iQPP, to foster future research.Comment: Accepted at SIGIR 202
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