Encoding Network News Today

We design a Kernelized Few-shot Object Detector by leveraging kernelized matrices computed over multiple proposal regions, which yield expressive non-linear representations whose model complexity is learned on the fly. Our pipeline contains several modules. An Encoding Network encodes support and query images. Our Kernelized Autocorrelation unit forms the linear, polynomial and RBF kernelized representations from features extracted within support regions of support images. These features are the...

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Abstract In this paper, we tackle a challenging problem of Few-shot Object Detection rather than recognition. We propose Power Normalizing Second-order Detector consisting of the Encoding Network (EN), the Multi-scale Feature Fusion (MFF), Second-order Pooling (SOP) with Power Normalization (PN), the Hyper Attention Region Proposal Network (HARPN) and Similarity N...