Exploring Tuning Characteristics of Ventral Stream's Neurons for Few-Shot Image Classification

Human has the remarkable ability of learning novel objects by browsing extremely few examples, which may be attributed to the generic and robust feature extracted in the ventral stream of our brain for representing visual objects. In this sense, the tuning characteristics of ventral stream's neurons can be useful prior knowledge to improve few-shot classification. Specifically, we computationally model two groups of neurons found in ventral stream which are respectively sensitive to shape cues and color cues. Then we propose the hierarchical feature regularization method with these neuron models to regularize the backbone of a few-shot model, thus making it produce more generic and robust features for few-shot classification. In addition, to simulate the tuning characteristic that neuron firing at a higher rate in response to foreground stimulus elements compared to background elements, which we call belongingness, we design a foreground segmentation algorithm based on the observation that the foreground object usually does not appear at the edge of the picture, then multiply the foreground mask with the backbone of few-shot model. Our method is model-agnostic and can be applied to few-shot models with different backbones, training paradigms and classifiers.