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@INPROCEEDINGS{Tommasi_BMVC_2009,
         author = {Tommasi, Tatiana and Caputo, Barbara},
       projects = {Idiap, DIRAC, EMMA},
          title = {The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories},
      booktitle = {British Machine Vision Conference},
           year = {2009},
       abstract = {Learning a category from few examples is a challenging task for vision algorithms,
while psychological studies have shown that humans are able to generalise correctly
even from a single instance (one-shot learning). The most accredited hypothesis is that
humans are able to exploit prior knowledge when learning a new related category. This
paper presents an SVM-based model adaptation algorithm able to perform knowledge
transfer for a new category when very limited examples are available. Using a leave-
one-out estimate of the weighted error-rate the algorithm automatically decides from
where to transfer (on which known category to rely,',','),
 how much to transfer (the degree
of adaptation) and if it is worth transferring something at all. Moreover a weighted
least-squares loss function takes optimally care of data unbalance between negative and
positive examples. Experiments presented on two different object category databases
show that the proposed method is able to exploit previous knowledge avoiding negative
transfer. The overall classification performance is increased compared to what would
be achieved by starting from scratch. Furthermore as the number of already learned
categories grows, the algorithm is able to learn a new category from one sample with
increasing precision, i.e. it is able to perform one-shot learning.},
            pdf = {https://publications.idiap.ch/attachments/papers/2009/Tommasi_BMVC_2009.pdf}
}