Unverified50% confidenceFactExact time
In multimodal models, a visual encoder (such as a ViT) maps image patches or video frames into continuous vectors, and the dimensional space these vectors inhabit is called latent space.
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50%
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Long-term
Relevance
10/28/2026
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Related Claims
VerifiedMultimodal large models split images into fixed patches, convert them into high-dimensional vectors via a visual encoder (based on the ViT architecture), and align them to the language model's token embedding space—this is inference, not reading.76% similarVerifiedThe core idea of ViT is to divide an image into fixed-size patches, flatten each patch and treat it as a token in a sequence, and then model global dependencies through a multi-head self-attention mechanism.73% similarUnverified空间比例理解依赖视觉Transformer（ViT）对图像空间结构的解析能力和扩散模型的条件控制能力72% similarUnverifiedThe vision encoder is typically composed of a convolutional neural network (CNN) or a vision Transformer (ViT), which cuts the image into several patches or regions and extracts high-dimensional feature vectors patch by patch.69% similarUnverifiedThe scenario embedding is a low-dimensional or medium-dimensional vector, obtained by mapping the hidden states through pooling, linear projection, or small attention modules.69% similar
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