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Transfer Learning in Sign language
2007
2007 IEEE Conference on Computer Vision and Pattern Recognition
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
doi:10.1109/cvpr.2007.383346
dblp:conf/cvpr/FarhadiFW07
fatcat:vm55ghpdb5hfvcvbde67ifoexq