Computer Vision and Pattern Recognition
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Efficient and Training-Free Single-Image Diffusion Models
Computer Science > Computer Vision and Pattern Recognition [Submitted on 3 Jun 2026] Title:Efficient and Training-Free Single-Image Diffusion Models View PDF HTML (experimental)Abstract:We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image. Recent approaches address this problem by training a diffusion model on a single image.
Efficient and accurate neural-field reconstruction using resistive memory
Abstract Applications such as medical imaging, augmented and virtual reality, and embodied artificial intelligence (AI) depend on the ability to reconstruct complex signals from sparse observations. These applications are characterized by incomplete measurements and limited computational resources. Traditional approaches to digital hardware face the following challenges: explicit signal representations require heavy sampling and storage, data movement across the von Neumann bottleneck...
Vision Hopfield Memory Networks for Image Recognition
Announce Type: replace Abstract: Recent vision backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress on image recognition. Despite their empirical success, these architectures remain far from the computational principles of the human brain, often demanding enormous amounts of training data while offering limited interpretability. We propose the Vision Hopfield Memory Network (V-HMN), a brain-inspired vision backbone that integrates...
Visual AI tracks nearly 100 wildlife species to improve conservation
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Tessera AI model offers accessible way to view Earth
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A walking tour of surveillance infrastructure in Seattle
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Human-Like Neural Nets by Catapulting
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