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Few-Shot learning aims to train and optimize a model that can adapt to unseenvisual classes with only a few labeled examples. The existing few-shot learning(FSL) methods, heavily rely only on visual data, thus fail to capture thesemantic attributes to learn a more generalized version of the visual conceptfrom very few examples. However, it is a known fact that human visual learningbenefits immensely from inputs from multiple modalities such as vision,language, and audio. Inspired by the human learning nature of encapsulating theexisting knowledge of a visual category which is in the form of language, weintroduce a contrastive alignment mechanism for visual and semantic featurevectors to learn much more generalized visual concepts for few-shot learning.Our method simply adds an auxiliary contrastive learning objective whichcaptures the contextual knowledge of a visual category from a strong textualencoder in addition to the existing training mechanism. Hence, the approach ismore generalized and can be plugged into any existing FSL method. Thepre-trained semantic feature extractor (learned from a large-scale textcorpora) we use in our approach provides a strong contextual prior knowledge toassist FSL. The experimental results done in popular FSL datasets show that ourapproach is generic in nature and provides a strong boost to the existing FSLbaselines.

Visual-Semantic Contrastive Alignment for Few-Shot Image Classification