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Hardware-Friendly Token-Group Activation Quantization for Low-Bit Mamba Super-Resolution
Mamba-based super-resolution models are efficient alternatives to Transformer-based restoration backbones, but their low-bit post-training quantization remains challenging because selective scan, Mamba's input-dependent state-space operator, induces different activation ranges across spatial tokens.
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