I can’t see where in the paper Data2Vec is reported to have contextual embeddings that work across modalities. Can you reference the section in the paper?
Data2Vec uses a “standard” transformer architecture.[1]
In Data2Vec2 the transformer architecture forms the “bulk of the model weights”.[2]
The tweaks I eluded to should have more clearly referenced that each modality uses a different encoder.
[1] section 3.1 data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language.
[2] section 3.2 Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language.
I’ve only scanned the blog but have read OG Data2Vec paper.
Data2Vec presents an architecture that performs well across the main benchmarks for vision, speech and text. The architecture is a variation on the transformer network with slight tweaks for each learning modality. Data2Vec 2 seems to be more a more efficient variant.
In terms of applications, data2vec gives a single reliable architecture for each approach. Whereas before you may have used a CNN for vision and a transformer for text etc.
Additionally, this research is building towards multi-modal learning where an architecture could be trained on images, text and speech to learn about a topic. (But to my knowledge there isn’t anything ground breaking in this space yet).