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 learning. In this paper, we design an automatic way called Semantic
Evolution to generate high-quality semantics. The incorporation of high-quality
semantics alleviates the need for complex network structures and learning
algorithms used in previous works. Hence, we employ a simple two-layer network
termed Semantic Alignment Network to transform semantics and visual features
into robust class prototypes with rich discriminative features for few-shot
classification. The experimental results show our framework outperforms all
previous methods on five benchmarks, demonstrating a simple network with
high-quality semantics can beat intricate multi-modal modules on few-shot
classification tasks.

利用少量数据进行学习是一项具有挑战性的计算机视觉任务，本文通过引入高质量的语义以及使用简单的网络结构，设计了一个名为 “语义进化” 的自动化方式来解决少样本学习中的问题，实验证明该方法在少样本分类任务中表现优异。