Unverified50% confidenceFactExact time
The 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).
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50%
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Long-term
Relevance
10/17/2026
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UnverifiedThe projection layer aligns visual feature vectors to the language model's token embedding space through a trainable multilayer perceptron or linear mapping.77% similarVerifiedMultimodal 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.77% similarUnverifiedWhen text instructions precede image input, the Transformer's cross-attention mechanism causes the model to have already formed clear query vectors when encoding the image, preferentially activating visual regions semantically related to the target.77% similarUnverifiedVisual consistency can be addressed through technical approaches such as temporal attention mechanisms, reference image anchoring, and latent space interpolation.76% similarUnverifiedContext awareness means that an inpainting model, when generating new content, simultaneously sees the original pixels outside the mask and infers from them the lighting, perspective and texture that the region should have; the encoder takes the entire image together with the mask as input.74% similar
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