Flamingo
A vision-language cross-modal model introduced by DeepMind, capable of joint understanding of images and text. It is a representative early work in multimodal AI research.
Core Facts
Timeline (last 90 days)
Interleaved Image-Text Alignment draws on the idea of interleaved image-text pretraining, such as the cross-modal attention structures adopted in works like Flamingo and OpenFlamingo.
Alignment between visual embedding vectors and text embedding vectors is typically accomplished through a projection layer, with common implementations including a linear layer, MLP, Q-Former (as used in BLIP-2), or Perceiver Resampler (as used in Flamingo).
The Flamingo model released by the DeepMind team in 2022 pioneered the few-shot learning paradigm for vision-language models.
Flamingo was proposed by DeepMind in 2022 and uses a Perceiver Resampler and cross-attention layers to achieve deep fusion of vision and language.
Flamingo was proposed by DeepMind in 2022, and BLIP-2 was proposed by Salesforce in 2023.
DeepMind's Flamingo injects visual features into the intermediate layers of a frozen language model using gated cross-attention layers.
DeepMind's Flamingo uses a Perceiver Resampler to bridge vision and language through cross-attention.
All Facts (8)
The technical foundation of multimodal large models is the joint training of a visual encoder (such as ViT) and a language model, enabling the model to map visual information from pixel space into a semantic space shared with text
75%UnverifiedDeepMind's Flamingo uses a Perceiver Resampler to bridge vision and language through cross-attention.
60%UnverifiedInterleaved Image-Text Alignment draws on the idea of interleaved image-text pretraining, such as the cross-modal attention structures adopted in works like Flamingo and OpenFlamingo.
50%UnverifiedAlignment between visual embedding vectors and text embedding vectors is typically accomplished through a projection layer, with common implementations including a linear layer, MLP, Q-Former (as used in BLIP-2), or Perceiver Resampler (as used in Flamingo).
50%UnverifiedThe Flamingo model released by the DeepMind team in 2022 pioneered the few-shot learning paradigm for vision-language models.
50%UnverifiedFlamingo was proposed by DeepMind in 2022 and uses a Perceiver Resampler and cross-attention layers to achieve deep fusion of vision and language.
50%UnverifiedFlamingo was proposed by DeepMind in 2022, and BLIP-2 was proposed by Salesforce in 2023.
50%UnverifiedDeepMind's Flamingo injects visual features into the intermediate layers of a frozen language model using gated cross-attention layers.
50%