Unverified50% confidenceFactExact time
Multimodal large models such as Gemini encode visual information into token sequences and feed them together with text tokens into a Transformer architecture for joint processing, but the Transformer context window is limited, and large amounts of low-level pixel details are lost after visual information is encoded and compressed.
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50%
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Long-term
Relevance
9/1/2026
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Related Claims
UnverifiedGemini's multimodal capabilities are based on a large-scale Transformer architecture capable of simultaneously processing inputs from different modalities including text, images, and audio.78% similarVerifiedThe Gemini series is natively multimodal by design, encoding data from different modalities such as text, images, audio, and video into a unified feature space.73% similarUnverifiedGemini架构通过视觉编码器（Vision Encoder）与语言模型的联合推理实现跨模态对齐（Cross-modal Alignment）能力71% similarVerifiedThe Gemini series jointly models multiple modalities from the training stage, rather than appending a visual encoder as solutions like GPT-4V do.69% similarVerifiedGemini adopts a native multimodal architecture, jointly training the Transformer with mixed multimodal data from the pre-training stage, with tokens of different modalities processed together in the same sequence.68% similar
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