LeAVJEPA: A Minimalist Architecture for Audio-Visual Self-Supervised Learning

Abstract:Prior audio-visual self-supervised learning methods rely on mechanisms such as EMA target encoders, prediction heads, reconstruction decoders, and contrastive losses. We introduce LeAVJEPA, the first audio-visual encoder trained under LeJEPA's collapse-free objective. A single early-fusion Vision Transformer processes audio, video, and joint audio-video inputs. Modality dropout treats a missing modality as another view of the same event, making cross-modal alignment implicit in the objective. The model aligns global embeddings with modality-specific local embeddings, and SIGReg prevents representational collapse. A controlled ablation identifies modality dropout as the key mechanism for audio-visual alignment. Despite the architectural simplicity, LeAVJEPA reaches 36.0 mAP on AudioSet-20K and 91.3% accuracy on ESC-50 under frozen evaluation. After fine-tuning, it reaches 61.1% accuracy on VGGSound, and its embeddings support zero-shot audio-visual retrieval.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2610.06226 [cs.LG]

(or arXiv:2610.06226v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.06226 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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