7 research outputs found
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities
Multimodal datasets contain an enormous amount of relational information,
which grows exponentially with the introduction of new modalities. Learning
representations in such a scenario is inherently complex due to the presence of
multiple heterogeneous information channels. These channels can encode both (a)
inter-relations between the items of different modalities and (b)
intra-relations between the items of the same modality. Encoding multimedia
items into a continuous low-dimensional semantic space such that both types of
relations are captured and preserved is extremely challenging, especially if
the goal is a unified end-to-end learning framework. The two key challenges
that need to be addressed are: 1) the framework must be able to merge complex
intra and inter relations without losing any valuable information and 2) the
learning model should be invariant to the addition of new and potentially very
different modalities. In this paper, we propose a flexible framework which can
scale to data streams from many modalities. To that end we introduce a
hypergraph-based model for data representation and deploy Graph Convolutional
Networks to fuse relational information within and across modalities. Our
approach provides an efficient solution for distributing otherwise extremely
computationally expensive or even unfeasible training processes across
multiple-GPUs, without any sacrifices in accuracy. Moreover, adding new
modalities to our model requires only an additional GPU unit keeping the
computational time unchanged, which brings representation learning to truly
multimodal datasets. We demonstrate the feasibility of our approach in the
experiments on multimedia datasets featuring second, third and fourth order
relations
Social-sensed image search
10.1145/2590974ACM Transactions on Information Systems322-ATIS
ACM Transactions on Information Systems : Vol. 32, No. 2, April 2014
1. Efficient Index-Based Snippet Generation / H. Bast, M. Celikik
2. Modelling Term Associations for Probabilistic Information Retrieval / J. Zhao, J.X. Huang, Z. Ye
3. Social-Sensed Image Search / P. Cui, et al.
4. Theoritical, Qualitative and Quantitative Analyses of Small-Document Approaches to Resources Selection / I. Markov, F. Crestan