FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

2026-09-10

AI Summary

本文提出FreeFlow，一种无偏的分层Transformer模型用于光流估计，通过三种注意力机制结合，在不依赖特定任务假设的情况下达到顶级性能。

Abstract

Optical flow methods typically rely on task-specific inductive biases, such as correlation volumes, feature warping, and iterative refinement, among others, to reach high accuracy. While effective, such biases constrain the model to predefined heuristics, which can limit its expressivity and lead to more complex pipelines and additional computational cost. We present FreeFlow, a hierarchical transformer built without any flow-specific components, using instead a single feed-forward encoder--decoder. FreeFlow combines three attention variants: window attention for local processing, shifted-window attention for cross-window information exchange, and a global attention operating at a reduced resolution. The resulting architecture scales naturally with model capacity, enabling a consistent accuracy gain from small to large variants. Despite the absence of standard inductive biases, FreeFlow achieves state-of-the-art results on major benchmarks, including Sintel (0.68/1.48 EPE on Clean/Final), KITTI-2015 (3.23 Fl-all), and Spring (3.192 1px), while remaining memory efficient at 1080p inference.

Problem

• Optical Flow
• Inductive Biases
• Hierarchical Transformer

Innovation

• Bias-free
• Hierarchical Transformer
• Optical Flow Estimation
• Attention Variants