Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …

This work introduces a model that can recognize objects in images even if no training data is
available for the object class. The only necessary knowledge about unseen categories …

In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of
multiple data modalities has the potential to reshape various applications. This paper …

Distributional semantics is a usage-based model of meaning, based on the assumption that
the statistical distribution of linguistic items in context plays a key role in characterizing their …

Distributional semantic models derive computational representations of word meaning from
the patterns of co-occurrence of words in text. Such models have been a success story of …

Recent work has shown that the integration of visual information into text-based models can
substantially improve model predictions, but so far only visual information extracted from …

We extend the SKIP-GRAM model of Mikolov et al.(2013a) by taking visual information into
account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based …

The lexicon of any natural language encodes a huge number of distinct word meanings. Just
to understand this article, you will need to know what thousands of words mean. The space …

Our research aims at building computational models of word meaning that are perceptually
grounded. Using computer vision techniques, we build visual and multimodal distributional …