Effective Multi-Modal Retrieval based on Stacked Auto-Encoders

Summary: Proposes an effective multi-modal retrieval framework using stacked auto-encoders to map heterogeneous media features into a shared low-dimensional space for cross-modal similarity search. Introduces a novel objective that models intra- and inter-modal semantics with minimal prior knowledge, and uses mini-batch, memory-efficient training, achieving state-of-the-art accuracy on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10942
Venue: VLDB
Year: 2014
Pagerank: 4.1945683e-05
Overall Rank: 12,016 | 16.41%
DOI: -

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Rank	Citing Paper	Year	Venue	Pagerank

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
79	A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces	1998	VLDB	0.00056242144
3,510	Inter-Media Hashing for Large-scale Retrieval from Heterogeneous Data Sources	2013	SIGMOD	7.0258619e-05

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