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Non-autoregressive Sequence-to-Sequence Vision-Language Models
Sequence-to-sequence vision-language models are showing promise, but their applicability is limited by their inference latency due to their autoregressive way of generating predictions.
MATrIX -- Modality-Aware Transformer for Information eXtraction
In these, text semantics and visual information supplement each other to provide a global understanding of the document.
Contrastive Neighborhood Alignment
We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features whereby data points that are mapped to nearby representations by the source (teacher) model are also mapped to neighbors by the target (student) model.
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