  Recognition of remote sensing (RS) or aerial images is currently of great
interest, and advancements in deep learning algorithms added flavor to it in
recent years. Occlusion, intra-class variance, lighting, etc., might arise
while training neural networks using unimodal RS visual input. Even though
joint training of audio-visual modalities improves classification performance
in a low-data regime, it has yet to be thoroughly investigated in the RS
domain. Here, we aim to solve a novel problem where both the audio and visual
modalities are present during the meta-training of a few-shot learning (FSL)
classifier; however, one of the modalities might be missing during the
meta-testing stage. This problem formulation is pertinent in the RS domain,
given the difficulties in data acquisition or sensor malfunctioning. To
mitigate, we propose a novel few-shot generative framework, Hallucinated
Audio-Visual Embeddings-Network (HAVE-Net), to meta-train cross-modal features
from limited unimodal data. Precisely, these hallucinated features are
meta-learned from base classes and used for few-shot classification on novel
classes during the inference phase. The experimental results on the benchmark
ADVANCE and AudioSetZSL datasets show that our hallucinated modality
augmentation strategy for few-shot classification outperforms the classifier
performance trained with the real multimodal information at least by 0.8-2%.
