Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its $\textit{modality-aligned}$ setting, $\textit{i.e.}$, the audio and visual modality are $\textit{both}$ assumed to signal the prediction target.With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored $\textit{unaligned}$ setting, where the goal is to recognize audio and visual events in a video with only weak labels observed.Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both).To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed $\textbf{V}$isual-$\textbf{A}$udio $\textbf{L}$abel Elab$\textbf{or}$ation (VALOR), is innovated to harvest modality labels for the training events.Empirical studies show that the harvested labels significantly improve an attentional baseline by $\textbf{8.0}$ in average F-score (Type@AV).Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality.Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin ($\textbf{+5.4}$

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its \textit{modality-aligned} setting, \textit{i.e.}, the audio and visual modality are \textit{both} assumed to signal the prediction target.With the Look, Listen, and Parse dataset (LLP), we investigate the under-explored \textit{unaligned} setting, where the goal is to recognize audio and visual events in a video with only weak labels observed.Such weak video-level labels only tell what events happen without knowing the modality they are perceived (audio, visual, or both).To enhance learning in this challenging setting, we incorporate large-scale contrastively pre-trained models as the modality teachers. A simple, effective, and generic method, termed \textbf{V} isual- \textbf{A} udio \textbf{L} abel Elab \textbf{or} ation (VALOR), is innovated to harvest modality labels for the training events.Empirical studies show that the harvested labels significantly improve an attentional baseline by \textbf{8.0} in average F-score (Type@AV).Surprisingly, we found that modality-independent teachers outperform their modality-fused counterparts since they are noise-proof from the other potentially unaligned modality.Moreover, our best model achieves the new state-of-the-art on all metrics of LLP by a substantial margin ( \textbf{ 5.4} F-score for Type@AV). VALOR is further generalized to Audio-Visual Event Localization and achieves the new state-of-the-art as well.