Biblio

In this paper, we present the preliminary design and implementation of SDN-SAVI, an SDN application that enables SAVI functionalities in SDN networks. In this proposal, all the functionalities are implemented on the controller without modifying SDN switches. To enforce SAVI on packets in the data plane, the controller installs binding tables in switches using existing SDN techniques, such as OpenFlow. With SDN-SAVI, a network administrator can now enforce SAVI in her network by merely integrating a module on the controller, rather than purchasing SAVI-capable switches and replacing legacy ones.

This paper focuses on a practically very important problem of matching a real-world product photo to exactly the same item(s) in online shopping sites. The task is extremely challenging because the user photos (i.e., the queries in this scenario) are often captured in uncontrolled environments, while the product images in online shops are mostly taken by professionals with clean backgrounds and perfect lighting conditions. To tackle the problem, we study deep network architectures and training schemes, with the goal of learning a robust deep feature representation that is able to bridge the domain gap between the user photos and the online product images. Our contributions are two-fold. First, we propose an alternative of the popular contrastive loss used in siamese deep networks, namely robust contrastive loss, where we "relax" the penalty on positive pairs to alleviate over-fitting. Second, a multi-task fine-tuning approach is introduced to learn a better feature representation, which not only incorporates knowledge from the provided training photo pairs, but also explores additional information from the large ImageNet dataset to regularize the fine-tuning procedure. Experiments on two challenging real-world datasets demonstrate that both the robust contrastive loss and the multi-task fine-tuning approach are effective, leading to very promising results with a time cost suitable for real-time retrieval.