NeoMME: an efficient Multimodal-native and Multilingual Encoder

Researchers have introduced NeoMME, a single-tower foundation encoder designed to unify multimodal and multilingual processing within a more efficient architecture. By streamlining the structure typically found in complex vision-language models, this approach aims to reduce the computational demands required for fine-tuning and inference tasks.