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Multimodal Prediction based on Graph Representations
Performed experiments in the context of multiple multimodal and visual datasets, as well as several descriptors and retrieval models, demonstrate that our learning model is highly effective for different prediction scenarios involving visual, textual, and multimodal features, yielding better effectiveness than state-of-the-art methods.
Fusion vectors: Embedding Graph Fusions for Efficient Unsupervised Rank Aggregation
We propose an innovative rank aggregation function that is unsupervised, intrinsically multimodal, and targeted for fast retrieval and top effectiveness performance.
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks.
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