MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning
arXiv:2606.25225v1 Announce Type: cross Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to jointly learn from both modalities is a natural next step, yet it remains challenging. Existing audio-visual self-supervised methods rely on modality-specific encoders and complex combinations of contrastive or reconstruction objectives, limiting cross-modal synergy and scalability. Joint Embedding Predictive Architectures (JEPAs) of
The paper addresses the current challenges in scalable audio-visual self-supervised learning, building on the success of visual representation learning and the natural co-occurrence of audio and visual streams in video data.
JEPAs represent a significant step towards more unified and scalable multimodal AI, potentially simplifying complex model architectures and accelerating the development of more human-like perception in AI systems.
Current fragmented approaches to audio-visual self-supervised learning, relying on modality-specific encoders and complex objectives, could be replaced by more integrated and scalable Joint Embedding Predictive Architectures (JEPAs).
- · AI researchers and developers
- · Multimodal AI applications
- · Companies with large video datasets
- · Hardware manufacturers for AI (GPUs, TPUs)
- · Developers of highly specialized, single-modality AI
- · Complex, multi-objective multimodal AI frameworks
MJEPA provides a simpler and more scalable architecture for integrated audio-visual learning.
This advancement could lead to AI systems with richer, more robust understanding of real-world phenomena through combined sensory input.
More efficient multimodal learning could accelerate the development of advanced AI agents capable of perceiving and interacting with environments in more nuanced ways.
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Read at arXiv cs.LG