FlowBench: Benchmarking Optical Flow Estimation Methods for Reliability and Generalization

TMLR Paper4358 Authors

Abstract:

Optical flow estimation is a crucial computer vision task often applied to safety-critical real-world scenarios like autonomous driving and medical imaging.
While optical flow estimation accuracy has greatly benefited from the emergence of deep learning, learning-based methods are also known for their lack of generalization and reliability.
However, reliability is paramount when optical flow methods are employed in the real world, where safety is essential.
Furthermore, a deeper understanding of the robustness and reliability of learning-based optical flow estimation methods is still lacking, hindering the research community from building methods safe for real-world deployment.
Thus we propose FlowBench, a robustness benchmark and evaluation tool for learning-based optical flow methods.
FlowBench facilitates streamlined research into the reliability of optical flow methods by benchmarking their robustness to adversarial attacks and out-of-distribution samples.
With FlowBench, we benchmark 63 methods across 3 different datasets under 7 diverse adversarial attacks and 23 established common corruptions, making it the most comprehensive robustness analysis of optical flow methods to date.
Across this wide range of methods, we consistently find that methods with state-of-the-art performance on established standard benchmarks lack reliability and generalization ability.
Moreover, we find interesting correlations between the performance, reliability, and generalization ability of optical flow estimation methods, under various lenses such as design choices used, number of parameters, etc.
After acceptance, FlowBench will be open-source and publicly available, including all tested model weights.

Submission Length: Long submission (more than 12 pages of main content)

Assigned Action Editor: Jia-Bin Huang

Submission Number: 4358