Paper:
Transfer Learning in a Transductive Setting
Category models for objects or activities typically rely on supervised learning requiring sufficiently large training sets. Transferring knowledge from known categories to novel classes with no or only a few labels is far less researched even though it is a common scenario. In this work, we extend transfer learning with semi-supervised learning to exploit unlabeled instances of (novel) categories with no or only a few labeled instances. Our proposed approach Propagated Semantic Transfer combines three techniques. First, we transfer information from known to novel categories by incorporating external knowledge, such as linguistic or expertspecified information, e.g., by a mid-level layer of semantic attributes. Second, we exploit the manifold structure of novel classes. More specifically we adapt a graph-based learning algorithm -so far only used for semi-supervised learningto zero-shot and few-shot learning. Third, we improve the local neighborhood in such graph structures by replacing the raw feature-based representation with a mid-level object-or attribute-based representation. We evaluate our approach on three challenging datasets in two different applications, namely on Animals with Attributes and ImageNet for image classification and on MPII Composites for activity recognition. Our approach consistently outperforms state-of-the-art transfer and semi-supervised approaches on all datasets.

Review:
This paper describes how to attack the zero-, one-, or 
      few-shot recognition problem, where we have a fair amount of training data 
      for some classes, but none or very few for some other classes. It does 
      this using three different techniques, all combined in a single framework: 
      using semantically-meaningful mid-layer knowledge (attributes), building a 
      graph on new classes to exploit the manifold structure, and finally by 
      using an attribute-based representation for building the graph structure 
      (rather than low-level features), which improves performance. The method 
      is evaluated on 3 different datasets (Animals with Attributes, ImageNet, 
      and MPII Cooking composites), and shows improved performance on all 
      compared to the state-of-the-art (slightly). On the plus side, the 
      paper's methods are well-motivated and can fit into the same optimization 
      framework, regardless of which kinds of side- or ground truth-annotations 
      are available. The experiments appear to be conducted fairly and show 
      slight improvements on the existing state-of-the-art (significantly for 
      the MPII dataset). The paper is well-written. The main negative is 
      simply that it is somewhat surprising that performance is not improved 
      more by doing all of this. I also have some minor suggestions to make the 
      paper a bit easier to read. - Exploiting the similarities of new 
      classes w.r.t. existing classes seems very similar to the "simile 
      classifiers" of Kumar et al. in their ICCV 2009 paper "Face verification 
      using attribute and simile classifiers." - line 74: rewrite to 
      "...models do not have to be retrained..." - Figure 3b: reorder 
      legend to put red curves on top, then blue curves, then black - 
      Figure 4a: accuracy is misspelled - supplementary material table 2 
      caption: underlying is misspelled