  Learning to classify video data from classes not included in the training
data, i.e. video-based zero-shot learning, is challenging. We conjecture that
the natural alignment between the audio and visual modalities in video data
provides a rich training signal for learning discriminative multi-modal
representations. Focusing on the relatively underexplored task of audio-visual
zero-shot learning, we propose to learn multi-modal representations from
audio-visual data using cross-modal attention and exploit textual label
embeddings for transferring knowledge from seen classes to unseen classes.
Taking this one step further, in our generalised audio-visual zero-shot
learning setting, we include all the training classes in the test-time search
space which act as distractors and increase the difficulty while making the
setting more realistic. Due to the lack of a unified benchmark in this domain,
we introduce a (generalised) zero-shot learning benchmark on three audio-visual
datasets of varying sizes and difficulty, VGGSound, UCF, and ActivityNet,
ensuring that the unseen test classes do not appear in the dataset used for
supervised training of the backbone deep models. Comparing multiple relevant
and recent methods, we demonstrate that our proposed AVCA model achieves
state-of-the-art performance on all three datasets. Code and data are available
at \url{https://github.com/ExplainableML/AVCA-GZSL}.
