RelayVSR: Large-Small Model Collaboration for Efficient Real-World Video Super-Resolution

arXiv:2609.37850 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/kopperx/RelayVSR

Terminology

• Generative Model — 26× in this paper · explained in 2 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Distillation — 6× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 5× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Flow Matching — 5× in this paper · explained in 8 episodes
• Video-Aware Reference Optimization — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Joint Attention — 1× in this paper · explained in 2 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Objective Mismatch — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Winter Conference on Applications of Computer Vision — 1× in this paper

Sources

• LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Flow-Based Real-World Super-Resolution
• The relativistic discriminator: a key element missing from standard GAN
• OS-DiffVSR: Towards One-step Latent Diffusion Model for High-detailed Real-world Video Super-Resolution
• RFSR: Improving ISR Diffusion Models via Reward Feedback Learning
• Wan: Open and Advanced Large-Scale Video Generative Models
• SwiftVR: Real-Time One-Step Generative Video Restoration
• GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution
• OARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution

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