Muffins vs. Chihuahuas Revisited: Exploring Zero-Shot and Few-Shot CLIP-Based Methods for Similar Object Classification

Similar object classification represents an important problem in computer vision. While models based on convolutional neural networks (CNN) historically struggled to distinguish visually similar objects, advancements in Vision Transformers (ViT) and more recently in vision-language models like OpenAI's CLIP have reinvigorated interest in zero-shot and few-shot learning approaches. We explore methods that combine CLIP's zero-shot classification capabilities with a few-shot embedding comparison strategy. By balancing these two paradigms, a near-perfect accuracy was achieved for the similar object recognition benchmark problem muffins vs. chihuahuas. The quality of this approach is further validated on additional tasks, such as differentiating cats from dogs, achieving 99.35% accuracy. The presented results illustrate that the synergy of zero-shot and few-shot paradigms is highly effective in tackling similar object image classification challenges without specialized training.