Summary of V”mean”ba: Visual State Space Models Only Need 1 Hidden Dimension, by Tien-yu Chi et al.

V“Mean”ba: Visual State Space Models only need 1 hidden dimension

by Tien-Yu Chi, Hung-Yueh Chiang, Chi-Chih Chang, Ning-Chi Huang, Kai-Chiang Wu

First submitted to arxiv on: 21 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary Vision transformers excel in image processing due to their superior performance. However, self-attention’s quadratic complexity limits scalability and deployment on resource-constrained devices. State Space Models (SSMs) address this by introducing linear recurrence mechanisms, reducing sequence modeling complexity from quadratic to linear. SSMs have been extended to high-resolution vision tasks, but the linear recurrence mechanism struggles to fully utilize modern hardware’s matrix multiplication units, resulting in a computational bottleneck. To resolve this, we introduce VMeanba, a training-free compression method that eliminates channel dimensions in SSMs using mean operations. Our key observation is that output activations of SSM blocks exhibit low variances across channels. VMeanba leverages this property to optimize computation by averaging activation maps across the channel, reducing computational overhead without compromising accuracy. Evaluations on image classification and semantic segmentation tasks demonstrate up to a 1.12x speedup with less than a 3% accuracy loss when combined with 40% unstructured pruning.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper is about making computer vision models run faster on devices that use a lot of energy or have limited memory. These models are very good at recognizing images, but they can’t be used in some situations because they require too much processing power. The researchers created a new way to make these models work better on smaller devices by removing unnecessary information and combining calculations. They tested this new method on two image-related tasks and found that it made the models run up to 1.12 times faster without losing much accuracy.