human
read this paragraph "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 mod- els. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre- trained in two stages. The first stage boot- straps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-
language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer train- able parameters. We also demonstrate the model’s emerging capabilities of zero-shot image-to-text generation that can follow natural language in- structions." please generate question 
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open