  We propose a few-shot adaptation framework, which bridges zero-shot learning
and supervised many-shot learning, for semantic indexing of image and video
data. Few-shot adaptation provides robust parameter estimation with few
training examples, by optimizing the parameters of zero-shot learning and
supervised many-shot learning simultaneously. In this method, first we build a
zero-shot detector, and then update it by using the few examples. Our
experiments show the effectiveness of the proposed framework on three datasets:
TRECVID Semantic Indexing 2010, 2014, and ImageNET. On the ImageNET dataset, we
show that our method outperforms recent few-shot learning methods. On the
TRECVID 2014 dataset, we achieve 15.19% and 35.98% in Mean Average Precision
under the zero-shot condition and the supervised condition, respectively. To
the best of our knowledge, these are the best results on this dataset.
