FACTNet

Overview

FACTNet is a hybrid CNN–Transformer architecture designed for real-time video restoration. It couples the local feature extraction strengths of convolutional networks with the long-range modelling of Transformers, while a Flash-Attention-optimized attention mechanism keeps inference fast enough for real-time use.

Highlights

• Flash-Attention optimization reduces the memory and latency cost of self-attention, enabling real-time throughput.
• A CNN–Transformer hybrid design balances fine-grained detail recovery with global temporal consistency.
• Recognised with a Best Paper Award at its 2025 venue.

Why it matters

Real-time restoration unlocks practical deployment in streaming, surveillance, and low-quality video enhancement where latency budgets are tight. FACTNet shows that attention-heavy restoration can be made efficient without sacrificing quality.

Gallery

Presentation & videos