A no-fine-tune token filtering module placed before a vision transformer encoder keeps static high-attention tokens and object-region tokens, yielding a 2.8x inference speedup on SigLIP retrieval with negligible recall loss.
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Speed-up of Vision Transformer Models by Attention-aware Token Filtering
A no-fine-tune token filtering module placed before a vision transformer encoder keeps static high-attention tokens and object-region tokens, yielding a 2.8x inference speedup on SigLIP retrieval with negligible recall loss.