GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation
Boris Kriuk, Matey Yordanov

TL;DR
GeloVec introduces a higher-dimensional geometric smoothing framework for CNN-based image segmentation, improving boundary stability and feature coherence through advanced manifold relationships and multispatial transformations.
Contribution
It presents a novel geometric smoothing method using n-dimensional distances and tensorial projections, enhancing segmentation accuracy and stability over existing attention-based approaches.
Findings
Achieves up to 2.7% mIoU improvement on benchmark datasets.
Provides theoretical guarantees on segmentation stability via Riemannian geometry.
Maintains computational efficiency with parallelized geodesic transformations.
Abstract
This paper introduces GeloVec, a new CNN-based attention smoothing framework for semantic segmentation that addresses critical limitations in conventional approaches. While existing attention-backed segmentation methods suffer from boundary instability and contextual discontinuities during feature mapping, our framework implements a higher-dimensional geometric smoothing method to establish a robust manifold relationships between visually coherent regions. GeloVec combines modified Chebyshev distance metrics with multispatial transformations to enhance segmentation accuracy through stabilized feature extraction. The core innovation lies in the adaptive sampling weights system that calculates geometric distances in n-dimensional feature space, achieving superior edge preservation while maintaining intra-class homogeneity. The multispatial transformation matrix incorporates tensorial…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Medical Image Segmentation Techniques · Image and Object Detection Techniques
MethodsSoftmax · Attention Is All You Need
