We build word models for American Sign Language (ASL) that transfer between different signers and different aspects. This is advantageous because one could use large amounts of labelled avatar data in combination with a smaller amount of labelled human data to spot a large number of words in human data. Transfer learning is possible because we represent blocks of video with novel intermediate discriminative features based on splits of the data. By constructing the same splits in avatar and human data and clustering appropriately, our features are both discriminative and semantically similar: across signers similar features imply similar words. We demonstrate transfer learning in two scenarios: from avatar to a frontally viewed human signer and from an avatar to human signer in a 3/4 view.
The influence of semantic ambiguity on word identification processes was explored in a series of word naming and lexical-decision experiments. There was no reliable ambiguity ef...
Abstract Vector‐based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply ...
Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are...
The Bag-of-Words (BoW) model is a promising image representation technique for image categorization and annotation tasks. One critical limitation of existing BoW models is that ...
We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named enti...
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