Best Scientific Paper Award at IAPR ICPR’16

Dan Xu, Jingkuan Song, Xavier Alameda-Pineda, Elisa Ricci and Nicu Sebe

I happily announce that we received the Best Scientific Paper Award for

Multi-paced dictionary learning for cross-domain retrieval and recognition

at IAPR International Conference on Pattern Recognition 2016 (Cancun, Mexico) [1].

Abstract Several applications benefit from learning coupled representations able to describe data from multiple sources. For instance, cross-domain dictionary learning methods demonstrated to be particularly effective. In this paper we introduce Multi-Paced Dictionary Learning (MPDL) and propose an instantiation of it under the framework of cross-domain dictionary learning. MPDL is inspired by previous works on self-paced learning, a framework able to enhance the accuracy of conventional learning models by presenting the training data in a meaningful order, i.e. easy samples are provided first. However, most of existing self-paced learning methods only consider a single modality, while MPDL is specifically designed to assess the learning pace when data from multiple sources are available. We present the model and propose an efficient algorithm to learn the dictionaries and codes. The approach is validated via experiments on two different tasks, namely cross-media retrieval and sketch-to-photo face recognition, using publicly available datasets.


  1. D. Xu, J. Song, X. Alameda-Pineda, E. Ricci, and N. Sebe, “Multi-Paced Dictionary Learning for Cross-Domain Retrieval and Recognition,” in IEEE International Conference on Pattern Recognition, Cancun, Mexico, 2016. [ bib pdf ] Award Best Intel Scientific Award
        title={Multi-Paced Dictionary Learning for Cross-Domain Retrieval and Recognition},
        author={Dan Xu and Jingkuan Song and Xavier Alameda-Pineda and Elisa Ricci and Nicu Sebe},
        booktitle={IEEE International Conference on Pattern Recognition},
        address={Cancun, Mexico},
        award={Best Intel Scientific Award},

Category: Research

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