Merges neighboring visual tokens into fewer, wider ones so the language model reads a shorter sequence.
Goes back toSep 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
A full-resolution vision encoder emits far more tokens than a language model wants to attend over. Patch mergers (or pixel-shuffle/unshuffle stages) fuse each small neighborhood of visual tokens into one wider token. Commonly a 4× reduction. Before handing them to the LLM. It is a large inference-cost saving with modest information loss.
Share of new models that have included a patch merger over time.
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