Few Shot Adaptation for Multimedia Semantic Indexing
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.
ACM Multimedia