Channel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality
State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Duality (VSSD). Yet the low-bit post-training quantization (PTQ) behavior of VSSD remains insufficiently understood. A weight-activation split on VSSD-Tiny identifies activation quantization as the dominant low-bit bottleneck, while representative inputs to selected VSSD-backbone linear layers exhibit strong channel-wise magnitude variation and token-localized extremes. We propose the Channel-wise Token-balanced Output-Aware Clipping (CTOAC) method, which learns per-input-channel clipping bounds ...
| Authors | Jonghyeon Lim, Changhoon Yim |
|---|---|
| Primary category | cs.CV |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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