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arXiv · 2506.23532

GViT: Representing Images as Gaussians for Visual Recognition

Jefferson Hernandez·Ruozhen He·Guha Balakrishnan·Alexander C. Berg·Vicente Ordonez
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Abstract

We introduce GVIT, a classification framework that abandons conventional pixel or patch grid input representations in favor of a compact set of learnable 2D Gaussians. Each image is encoded as a few hundred Gaussians whose positions, scales, orientations, colors, and opacities are optimized jointly with a ViT classifier trained on top of these representations. We reuse the classifier gradients as constructive guidance, steering the Gaussians toward class-salient regions while a differentiable renderer optimizes an image reconstruction loss. We demonstrate that by 2D Gaussian input representations coupled with our GVIT guidance, using a relatively standard ViT architecture, closely matches the performance of a traditional patch-based ViT, reaching a 76.9% top-1 accuracy on Imagenet-1k using a ViT-B architecture.

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Jefferson Hernandez, Ruozhen He, Guha Balakrishnan, Alexander C. Berg, Vicente Ordonez. 2025-06-30. GViT: Representing Images as Gaussians for Visual Recognition. https://arxiv.org/abs/2506.23532

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