  Generalized Zero-Shot Learning (GZSL) is a challenging task requiring
accurate classification of both seen and unseen classes. Within this domain,
Audio-visual GZSL emerges as an extremely exciting yet difficult task, given
the inclusion of both visual and acoustic features as multi-modal inputs.
Existing efforts in this field mostly utilize either embedding-based or
generative-based methods. However, generative training is difficult and
unstable, while embedding-based methods often encounter domain shift problem.
Thus, we find it promising to integrate both methods into a unified framework
to leverage their advantages while mitigating their respective disadvantages.
Our study introduces a general framework employing out-of-distribution (OOD)
detection, aiming to harness the strengths of both approaches. We first employ
generative adversarial networks to synthesize unseen features, enabling the
training of an OOD detector alongside classifiers for seen and unseen classes.
This detector determines whether a test feature belongs to seen or unseen
classes, followed by classification utilizing separate classifiers for each
feature type. We test our framework on three popular audio-visual datasets and
observe a significant improvement comparing to existing state-of-the-art works.
Codes can be found in https://github.com/liuyuan-wen/AV-OOD-GZSL.
