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Tutorial on Diffusion Models for Imaging and Vision
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Tutorial on Diffusion Models for Imaging and Vision
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The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some shortcomings that were deemed difficult in the previous approaches. The goal of this tutorial is to discuss the essential ideas underlying the diffusion models. The target audience of this tutorial includes undergraduate and graduate students who are interested in doing research on diffusion models or applying these models to solve other problems.
Forward citations
Cited by 5 Pith papers
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Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
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Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments
Flow equivariant world models use a latent memory that shifts with the agent and with inferred object motion, giving stable long-horizon prediction under partial observability.
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Diffusion Policy Policy Optimization
DPPO fine-tunes diffusion policies via policy gradients and outperforms prior RL approaches for diffusion policies and PG-tuned alternatives on robot benchmarks while enabling stable training and hardware deployment.
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Scene-aware Prediction of Diverse Human Movement Goals
CVAE model predicts diverse human movement goals from RGB scenes and poses, demonstrating generalization on GTA-IM and PROX datasets.
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Dual-Stream EEG Decoding for 3D Visual Perception
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.
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