Paper:
Reshaping Visual Datasets for Domain Adaptation
In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting,  pose, background, resolution, etc.)  We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other to the maximum extent; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric formulation and efficient optimization procedure that can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks.

Review:
This paper presents a new technique for domain 
      adaptation for computer vision datasets, which typically present multiple 
      aspects, e.g. viewpoint, illumination, level of clutter, compression, etc. 
      Instead of simply equating domains with datasets, which ends up mixing 
      those aspects together, the proposed technique automatically partitions 
      the set of all images over all datasets into domains. The partitioner is 
      driven by two principles: making separate domains in feature space, and 
      making them so that a good discriminative classifier can be trained on 
      each of them (to classify the original classes, not to separate the 
      domains). This technique is more likely to partition according to the 
      underlying aspects rather than datasets. Originality and 
      significance: There is only little work on automatically defining 
      domains, and this paper proposes a good idea towards automatically 
      discovering useful domains, that will support training better classifiers 
      for the original problem. On the negative side, I am not fully 
      convinced of the proposed optimization method (section 2), as it's not 
      clear how closely it solves the original problem (2)+(3). Moreover, the 
      two proposed driving criteria are not well integrated yet: the maximal 
      learnability criterion is only used as a 'wrapper around' the maximal 
      separability criterion, in order to determine the number of domains. 
      Essentially, it acts as a post-hoc validation score deciding how good is 
      the domain partitioning learned for a given number of domains, but the 
      partitioning itself is made based on the separability criterion alone. An 
      integrated process would instead produce the partitioning that maximises 
      some goal function including both criteria directly. Despite these 
      shortcomings, which might be due to the fact that this type of work is 
      still at quite exploratory stages, I feel that the paper is a step in the 
      right direction for the community and should be accepted. 
      Quality and clarity: The paper is well written, but lacks 
      figures to illustrate the concepts presented. Experiments: 
      On the positive side, the experiments show a significant advantage in 
      using the domains produced by the proposed method, over just using 
      datasets as domains, and over the very recent domain discovery technique 
      [19] (sec. 4.2). The idea of including the test set in the 
      'reshaping' process is interesting, but not clearly presented (sec. 4.3). 
      Also, this corresponds to an imputation setting (i.e. all test data is 
      available at the same time), but this is not stated clearly. On 
      the negative side, the image descriptor used is very simple and outdated: 
      just one bag-of-words of sparse SURF features for the entire image, and 
      with just 800 codebook entries. This is really weak nowadays. I recommend 
      the authors to use a spatial pyramid of bag-of-words, computed on _dense_ 
      SURF features. Also, it is not clear what similarity measure is used to 
      compare image descriptors, hopefully X^2 or an intersection kernel? The 
      Euclidean distance is not suitable for comparing histograms. As a next 
      step, a Fisher Vector representation could help further and finally place 
      the image representation in this paper at the level of modern systems. The 
      following papers might help the authors: Zhang IJCV 2006; Jurie CVPR 2005; 
      Lazebnik CVPR 2006; Perronin ECCV 2010.