Paper:
A Unified Semantic Embedding: Relating Taxonomies and Attributes
We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes. Contrary to prior work, which only utilized them as side information, we explicitly embed these semantic entities into the same space where we embed categories, which enables us to represent a category as their linear combination. By exploiting such a unified model for semantics, we enforce each category to be generated as a supercategory + a sparse combination of attributes, with an additional exclusive regularization to learn discriminative composition. The proposed reconstructive regularization guides the discriminative learning process to learn a model with better generalization. This model also generates compact semantic description of each category, which enhances interoperability and enables humans to analyze what has been learned.

Review:
The paper presents a multi-task learning based technique for jointly accommodating information conveyed by mid-level attributes and category semantics. A sparse coding approach is utilized to infer discriminative information to identify categories while permitting generative modeling to reason out new categories. The method is evaluated on the Animals with attributes dataset and shows improved classification performance, with an emphasis on one/few-shot learning circumstances.The paper, as mentioned in the manuscript, is a culmination of different existing ideas pertaining to semantic models, discriminative embedding, multitask learning and sparse learning. While the paper does argue how they are different, in my opinion there isn't much contribution in terms of a new approach/algorithm.Moreover, the paper seems to overly use/rely on recent "terminologies" in the name of attributes/semantics, while in crux all its doing is taking different pieces of information about the entities (object categories, here) one is interested in. While one can argue, for instance, attributes are generic while category specific information is not, the approach is more like an hierarchical one where such information can be integrated. In that spirit, existing deep learning methods do the same thing without having to handcode these pieces of information. Also of relevance is the coarse-to-fine strategies that vision community has looked at for many years. Besides dealing with these features, the claim of jointly performing discriminative and generative modeling dates back atleast three decades in the pattern recognition literature eg [Ref1-4]. And in doing do, the paper resorts to existing sparse coding schemes - while it argues [14] learns dictionary in unsupervised way, there are efforts that learn dictionaries discriminatively (eg. [Ref5-6]). Thus there is not much new/interesting contribution in the paper.References:[Ref1] P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice-Hall International, Englewood Cliffs, NJ, 1980).[Ref2] K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Ed. (Academic Press, New York, 1990. [Ref3] R. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches (John Wiley & Sons, New York, 1992)[Ref4] Lasserre, Julia A., Christopher M. Bishop, and Thomas P. Minka. "Principled hybrids of generative and discriminative models." Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 1. IEEE, 2006.[Ref5] Jiang, Zhuolin, Zhe Lin, and Larry S. Davis. "Learning a discriminative dictionary for sparse coding via label consistent K-SVD." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.[Ref6] Yang, Meng, D. Zhang, and Xiangchu Feng. "Fisher discrimination dictionary learning for sparse representation." Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011.