# Adaptive Segmentation-Based Initialization for Steered Mixture of Experts Image Regression

arXiv: https://arxiv.org/abs/2409.10101 · HTML: https://mlsanity.com/paper/2409.10101/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts.
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Placed in

- Mixture-of-experts / [Vision mixture-of-experts](https://mlsanity.com/moe/vision-mixture-of-experts/)

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [ProPainter: Improving Propagation and Transformer for Video Inpainting](https://mlsanity.com/paper/2309.03897/) (2023)
- [Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2304.03322/) (2023)
- [Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger](https://mlsanity.com/paper/2302.14677/) (2023)
- [Diverse Inpainting and Editing with GAN Inversion](https://mlsanity.com/paper/2307.15033/) (2023)
- [Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression](https://mlsanity.com/paper/2207.12348/) (2022)
- [Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising](https://mlsanity.com/paper/2305.03485/) (2023)
- [Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference](https://mlsanity.com/paper/2303.17409/) (2023)
- [POLAFFINI: Efficient feature-based polyaffine initialization for improved non-linear image registration](https://mlsanity.com/paper/2407.03922/) (2024)
- [4-D Epanechnikov Mixture Regression in Light Field Image Compression](https://mlsanity.com/paper/2108.06464/) (2021)
- [Sparse Video Representation Using Steered Mixture-of-Experts With Global Motion Compensation](https://mlsanity.com/paper/2209.05993/) (2022)

## Built on by (most cited first, 3 of 3)

- [Segmentation-Driven Initialization for Sparse-view 3D Gaussian Splatting](https://mlsanity.com/paper/2509.11853/) (2025)
- [Rasterized Steered Mixture of Experts for Efficient 2D Image Regression](https://mlsanity.com/paper/2510.05814/) (2025)
- [Edge-Aligned Initialization of Kernels for Steered Mixture-of-Experts](https://mlsanity.com/paper/2602.02031/) (2026)

## Abstract (arXiv)

Kernel image regression methods have shown to provide excellent efficiency in many image processing task, such as image and light-field compression, Gaussian Splatting, denoising and super-resolution. The estimation of parameters for these methods frequently employ gradient descent iterative optimization, which poses significant computational burden for many applications. In this paper, we introduce a novel adaptive segmentation-based initialization method targeted for optimizing Steered-Mixture-of Experts (SMoE) gating networks and Radial-Basis-Function (RBF) networks with steering kernels. The novel initialization method allocates kernels into pre-calculated image segments. The optimal number of kernels, kernel positions, and steering parameters are derived per segment in an iterative optimization and kernel sparsification procedure. The kernel information from "local" segments is then transferred into a "global" initialization, ready for use in iterative optimization of SMoE, RBF, and related kernel image regression methods. Results show that drastic objective and subjective quality improvements are achievable compared to widely used regular grid initialization, "state-of-the-art" K-Means initialization and previously introduced segmentation-based initialization methods, while also drastically improving the sparsity of the regression models. For same quality, the novel initialization results in models with around 50% reduction of kernels. In addition, a significant reduction of convergence time is achieved, with overall run-time savings of up to 50%. The segmentation-based initialization strategy itself admits heavy parallel computation; in theory, it may be divided into as many tasks as there are segments in the images. By accessing only four parallel GPUs, run-time savings of already 50% for initialization are achievable.