How does the number of expert modules in a mixture-of-experts architecture affect cross-domain accuracy degrad

The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative

Research goal: How does the number of expert modules in a mixture-of-experts architecture affect cross-domain accuracy degradation when shifting from COCO to Conceptual Captions in multimodal vision-language models?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.0/10.