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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 ,