Researchers have developed a new post-training quantization (PTQ) method called Channel-wise Token-balanced Output-Aware Clipping (CTOAC) to address the low-bit quantization challenges in Visual State Space Duality (VSSD) models. The study identified activation quantization as a key bottleneck and proposed CTOAC to learn per-input-channel clipping bounds, minimizing reconstruction loss. This method successfully maintains ImageNet-1K accuracy and improves robustness at aggressive precision settings across various VSSD model sizes, while also preserving performance on COCO and ADE20K datasets for object detection and segmentation tasks. Deployments on RTX 4090 have shown up to a 1.42x speedup over FP32. AI
IMPACT Improves efficiency and robustness of vision models, potentially enabling wider deployment on resource-constrained hardware.
RANK_REASON The cluster describes a research paper detailing a new method for optimizing model quantization.
Read on Hugging Face Daily Papers →
- ADE20K
- Channel-wise Token-balanced Output-Aware Clipping
- COCO
- CTOAC
- Mamba
- RTX 4090
- Vim
- Visual State Space Duality
- VmambaSCI: Dynamic Deep Unfolding Network with Mamba for Compressive Spectral Imaging
- VSSD-Base
- VSSD-Small
- VSSD-Tiny
- ImageNet-1K
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