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"Kernelized Few-shot Object Detection with Efficient Integral Aggregati" by Shan Zhang, Lei Wang et al.

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 then cross-correlated against features of a query image to obtain attention weights, and generate query proposal regions via an Attention Region Proposal Net. As the query proposal regions are many, each described by the linear, polynomial and RBF kernelized matrices, their formation is costly but that cost is reduced by our proposed Integral Region-of-Interest Aggregation unit. Finally, the Multi-head Relation Net combines all kernelized (second-order) representations with the first-order feature ....

Kernelized Few Shot Object Detector , Encoding Network , Kernelized Autocorrelation , Attention Region Proposal , Integral Region Of Interest Aggregation , Multi Head Relation Net , Computer Vision Theory , Machine Learning , Ecognition Detection , Representation Learning ,

"Few-Shot Object Detection by Second-Order Pooling" by Shan Zhang, Dawei Luo et al.

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 Network (SN). EN takes support image crops and a query image per episode to produce covolutional feature maps across several layers while MFF combines them into multi-scale feature maps. SOP aggregates them per support image while PN detects the presence of visual feature instead of counting its frequency of occurrence. HARPN cross-correlates the PN pooled support features against the query feature map to match regions and produce query region proposals that are then aggregated with S ....

Similarity Network , Hyper Attention Region Proposal Network , Few Shot Object Detection , Power Normalizing Second Order Detector , Encoding Network , Multi Scale Feature Fusion , Second Order Pooling , Power Normalization ,