CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow
This work addresses a key problem in computer vision for applications like autonomous driving and video analysis, offering a robust solution for optical flow estimation in challenging conditions.
The paper tackles the challenge of accurately estimating optical flow with large displacements and motion blur by proposing CRAFT, a cross-attentional flow transformer that replaces dot-product correlations with transformer cross-frame attention, achieving new state-of-the-art performance on Sintel and KITTI benchmarks and demonstrating greater robustness under image shifting attacks compared to RAFT and GMA.
Optical flow estimation aims to find the 2D motion field by identifying corresponding pixels between two images. Despite the tremendous progress of deep learning-based optical flow methods, it remains a challenge to accurately estimate large displacements with motion blur. This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images. The locality of convolutional features makes the computed correlations susceptible to various noises. On large displacements with motion blur, noisy correlations could cause severe errors in the estimated flow. To overcome this challenge, we propose a new architecture "CRoss-Attentional Flow Transformer" (CRAFT), aiming to revitalize the correlation volume computation. In CRAFT, a Semantic Smoothing Transformer layer transforms the features of one frame, making them more global and semantically stable. In addition, the dot-product correlations are replaced with transformer Cross-Frame Attention. This layer filters out feature noises through the Query and Key projections, and computes more accurate correlations. On Sintel (Final) and KITTI (foreground) benchmarks, CRAFT has achieved new state-of-the-art performance. Moreover, to test the robustness of different models on large motions, we designed an image shifting attack that shifts input images to generate large artificial motions. Under this attack, CRAFT performs much more robustly than two representative methods, RAFT and GMA. The code of CRAFT is is available at https://github.com/askerlee/craft.