Verified80% confidenceFactExact time
Multimodal 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.
6
Sources
80%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedEmbedding models compress different types of content such as text, speech, and images into fixed-length numerical vectors, with semantically similar content having vectors that are closer together in high-dimensional space.79% similarUnverifiedThe projection layer aligns visual feature vectors to the language model's token embedding space through a trainable multilayer perceptron or linear mapping.78% similarUnverifiedThe current mainstream multimodal understanding approach is to align image features output by a visual encoder into the language model's embedding space via a projection layer or cross-attention, so that text tokens and image tokens participate in self-attention computation within the same sequence (early fusion).77% similarUnverifiedLLaVA-1.6's AnyRes strategy selects the optimal cropping scheme based on the image aspect ratio, passes each sub-patch independently through the visual encoder, while retaining one downsampled global image.76% similarUnverifiedIn 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.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/517920API
curl https://kongchang.com/api/v1/knowledge/claims/517920MCP
get_claim(id=517920)