One-Shot Image Recognition Using Prototypical Encoders With Reduced Hubness

Humans have the innate ability to recognize new objects just by looking at sketches of them (also referred as to proto-type images). Similarly, prototypical images can be used as an effective visual representations of unseen classes to tackle few-shot learning (FSL) tasks. Our main goal is to recognize unseen hand signs (gestures) traffic-signs, and corporate-logos, by having their iconographic images or prototypes. Previous works proposed to utilize variational prototypical-encoders (VPE) to address FSL problems. While VPE learns an image-to-image translation task efficiently, we discovered that its performance is significantly hampered by the so-called hubness problem and it fails to regulate the representations in the latent space. Hence, we propose a new model (VPE++) that inherently reduces hubness and incorporates contrastive and multi-task losses to increase the discriminative ability of FSL models. Results show that the VPE++ approach can generalize better to the unseen classes and can achieve superior accuracies on logos, traffic signs, and hand gestures datasets as compared to the state-of-the-art.