24 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
Blackthorn: Large-Scale Interactive Multimodal Learning
This paper presents Blackthorn, an efficient interactive multimodal learning approach facilitating analysis of multimedia collections of up to 100 million items on a single high-end workstation. Blackthorn features efficient data compression, feature selection, and optimizations to the interactive learning process. The Ratio-64 data representation introduced in this paper only costs tens of bytes per item yet preserves most of the visual and textual semantic information with good accuracy. The optimized interactive learning model scores the Ratio-64-compressed data directly, greatly reducing the computational requirements. The experiments compare Blackthorn with two baselines: Conventional relevance feedback, and relevance feedback using product quantization to compress the features. The results show that Blackthorn is up to 77.5× faster than the conventional relevance feedback alternative, while outperforming the baseline with respect to the relevance of results: It vastly outperforms the baseline on recall over time and reaches up to 108% of its precision. Compared to the product quantization variant, Blackthorn is just as fast, while producing more relevant results. On the full YFCC100M dataset, Blackthorn performs one complete interaction round in roughly 1 s while maintaining adequate relevance of results, thus opening multimedia collections comprising up to 100 million items to fully interactive learning-based analysis
Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images
International audienceIn this demonstration, we present Exquisitor, a media explorer capable of learning user preferences in real-time during interactions with the 99.2 million images of YFCC100M. Exquisitor owes its efficiency to innovations in data representation, compression, and indexing. Exquisitor can complete each interaction round, including learning preferences and presenting the most relevant results, in less than 30 ms using only a single CPU core and modest RAM. In short, Exquisitor can bring large-scale interactive learning to standard desktops and laptops, and even high-end mobile devices
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
The variety and complexity of relations in multimedia data lead to
Heterogeneous Information Networks (HINs). Capturing the semantics from such
networks requires approaches capable of utilizing the full richness of the
HINs. Existing methods for modeling HINs employ techniques originally designed
for graph neural networks, and HINs decomposition analysis, like using manually
predefined metapaths. In this paper, we introduce a novel prototype-enhanced
hypergraph learning approach for node classification in HINs. Using hypergraphs
instead of graphs, our method captures higher-order relationships among nodes
and extracts semantic information without relying on metapaths. Our method
leverages the power of prototypes to improve the robustness of the hypergraph
learning process and creates the potential to provide human-interpretable
insights into the underlying network structure. Extensive experiments on three
real-world HINs demonstrate the effectiveness of our method