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Paper

Know2Look: Commonsense Knowledge for Visual Search

MPS-Authors
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Nag Chowdhury,  Sreyasi
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Tandon,  Niket
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1909.00749.pdf
(Preprint), 2MB

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Citation

Nag Chowdhury, S., Tandon, N., & Weikum, G. (2019). Know2Look: Commonsense Knowledge for Visual Search. Retrieved from http://arxiv.org/abs/1909.00749.


Cite as: http://hdl.handle.net/21.11116/0000-0005-83D2-9
Abstract
With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval.