AV-JEPA: Extending LeJEPA to Audio-Visual Self-Supervised Learning
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Computer Science > Multimedia
Title:AV-JEPA: Extending LeJEPA to Audio-Visual Self-Supervised Learning
Abstract:We present AV-JEPA, an elegant multimodal extension of LeJEPA to audio-visual self-supervised learning. Using an early-fusion Vision Transformer and modality dropout as masking, the model is trained to align the embeddings of global and per-modality local views, while the SIGReg objective encourages a theoretically optimal distribution. This achieves cross-modal alignment in the latent space, resulting in a remarkably clean architecture with no decoder, EMA teacher, complex multi-term losses, or contrastive negatives. The proposed AV-JEPA backbone delivers competitive classification performance on VGGSound (57.1% top-1) and AudioSet (32.7 mAP) and supports zero-shot audio-video retrieval out of the box.
| Subjects: | Multimedia (cs.MM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2607.15295 [cs.MM] |
| (or arXiv:2607.15295v1 [cs.MM] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15295
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