Exploring large scale data for multimedia QA: an initial study
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Exploiting the entire feature space with sparsity for automatic image annotation
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Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme
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Image annotation by semi-supervised cross-domain learning with group sparsity
Journal of Visual Communication and Image Representation
Image classification with manifold learning for out-of-sample data
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Multi-instance multi-label learning with weak label
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation
Image and Vision Computing
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In this paper, we introduce the second version of Microsoft Research Asia Multimedia (MSRA-MM), a dataset that aims to facilitate research in multimedia information retrieval and related areas. The images and videos in the dataset are collected from a commercial search engine with more than 1000 queries. It contains about 1 million images and 20,000 videos. We also provide the surrounding texts that are obtained from more than 1 million web pages. The images and videos have been comprehensively annotated, including their relevance levels to corresponding queries, semantic concepts of images, and category and quality information of videos. We define six standard tasks on the dataset: (1) image search reranking; (2) image annotation; (3) query-by-example image search; (4) video search reranking; (5) video categorization; and (6) video quality assessment.