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We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approaches embed both queries and items through a small encoder and retrieve the ...

Cross-modal hashing has been widely investigated recently for its efficiency in large-scale cross-media retrieval. However, most existing cross-modal hashing methods learn hash functions in a batch-based learning mode. Such mode is not suitable for ...

With the growth of images on the Internet, plenty of hashing methods are developed to handle the large-scale image retrieval task. Hashing methods map data from high dimension to compact codes, so that they can effectively cope with complicated image ...

We propose an unsupervised hashing method, exploiting a shallow neural network, that aims to produce binary codes that preserve the ranking induced by an original real-valued representation. This is motivated by the emergence of small-world graph-based ...

Despite significant progress in supervised hashing, there are three common limitations of existing methods. First, most pioneer methods discretely learn hash codes bit by bit, making the learning procedure rather time-consuming. Second, to reduce the ...

We present a scalable approach to automatically suggest relevant clothing products, given a single image without metadata. We formulate the problem as cross-scenario retrieval: the query is a real-world image, while the products from online shopping ...

Places in movies and sitcoms could indicate higher-level semantic cues about the story scenarios and actor relations. This paper presents a novel unsupervised framework for efficient place retrieval in movies and sitcoms. We leverage face detection to ...

In this poster we describe alternative inverted index structures that reduce the time required to process queries, produce a higher query throughput and still return high quality results to the end user. We give results based upon the TREC Terabyte ...