Learning to recognize novel concepts from just a few image samples is very
challenging as the learned model is easily overfitted on the few data and
results in poor generalizability. One promising but underexplored solution is
to compensate the novel classes by generating plausible samples. However, most
existing works of this line exploit visual information only, rendering the
generated data easy to be distracted by some challenging factors contained in
the few available samples. Being aware of the semantic information in the
textual modality that reflects human concepts, this work proposes a novel
framework that exploits semantic relations to guide dual-view data
hallucination for few-shot image recognition. The proposed framework enables
generating more diverse and reasonable data samples for novel classes through
effective information transfer from base classes. Specifically, an
instance-view data hallucination module hallucinates each sample of a novel
class to generate new data by employing local semantic correlated attention and
global semantic feature fusion derived from base classes. Meanwhile, a
prototype-view data hallucination module exploits semantic-aware measure to
estimate the prototype of a novel class and the associated distribution from
the few samples, which thereby harvests the prototype as a more stable sample
and enables resampling a large number of samples. We conduct extensive
experiments and comparisons with state-of-the-art methods on several popular
few-shot benchmarks to verify the effectiveness of the proposed framework.

通过利用语义关系引导双视图数据幻影，为小样本图像识别提供更多样化和合理化的新数据样本，该框架能通过从基础类别中进行有效的信息传递生成新颖类别的样本。其中，实例视图数据幻影模块利用基础类别的局部语义相关注意力和全局语义特征融合生成新颖类别的每个样本；原型视图数据幻影模块利用语义感知度量来估计新颖类别的原型和相关分布，从而实现对大量样本的重新采样，提高样本的稳定性。通过在几个常用的小样本数据集上与最先进的方法进行了广泛的实验和比较，验证了该框架的有效性。