Simple Semantic-Aided Few-Shot Learning

arXiv:2311.18649v2 Announce Type: replace
Abstract: Learning from a limited amount of data, namely Few-Shot Learning, stands out as a challenging computer vision task. Several works exploit semantics and design complicated semantic fusion mechanisms to compensate for rare representative features within restricted data. However, relying on naive semantics such as class names introduces biases due to their brevity, while acquiring extensive semantics from external knowledge takes a huge time and effort. This limitation severely constrains the potential of semantics in few-shot …

arxiv cs.cv few-shot few-shot learning semantic simple type