Audio-visual generalised zero-shot learning for video classification requires understanding the relations between the audio and visual information in order to be able to recognise samples from novel, previously unseen classes at test time. The natural semantic and temporal alignment between audio and visual data in video data can be exploited to learn powerful representations that generalise to unseen classes at test time. We propose a multi-modal and Temporal Cross-attention Framework (TCaF) for audio-visual generalised zero-shot learning. Its inputs are temporally aligned audio and visual features that are obtained from pre-trained networks. Encouraging the framework to focus on cross-modal correspondence across time instead of self-attention within the modalities boosts the performance significantly. We show that our proposed framework that ingests temporal features yields state-of-the-art performance on the UCF-GZSL cls, VGGSound-GZSL cls, and ActivityNet-GZSL cls benchmarks for (generalised) zero-shot learning. Code for reproducing all results is available at https://github.com/ExplainableML/TCAF-GZSL.
CITATION STYLE
Mercea, O. B., Hummel, T., Koepke, A. S., & Akata, Z. (2022). Temporal and Cross-modal Attention for Audio-Visual Zero-Shot Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13680 LNCS, pp. 488–505). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20044-1_28
Mendeley helps you to discover research relevant for your work.