TransFlow: Transformer as Flow Learner

• URL: http://arxiv.org/abs/2304.11523v1
• Date: Sun, 23 Apr 2023 03:11:23 GMT
• Title: TransFlow: Transformer as Flow Learner
• Authors: Yawen Lu, Qifan Wang, Siqi Ma, Tong Geng, Yingjie Victor Chen, Huaijin Chen, and Dongfang Liu
• Abstract summary: We propose TransFlow, a pure transformer architecture for optical flow estimation. It provides more accurate correlation and trustworthy matching in flow estimation. It recovers more compromised information in flow estimation through long-range temporal association in dynamic scenes.
• Score: 22.727953339383344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement. In this work, we propose TransFlow, a pure transformer architecture for optical flow estimation. Compared to dominant CNN-based methods, TransFlow demonstrates three advantages. First, it provides more accurate correlation and trustworthy matching in flow estimation by utilizing spatial self-attention and cross-attention mechanisms between adjacent frames to effectively capture global dependencies; Second, it recovers more compromised information (e.g., occlusion and motion blur) in flow estimation through long-range temporal association in dynamic scenes; Third, it enables a concise self-learning paradigm and effectively eliminate the complex and laborious multi-stage pre-training procedures. We achieve the state-of-the-art results on the Sintel, KITTI-15, as well as several downstream tasks, including video object detection, interpolation and stabilization. For its efficacy, we hope TransFlow could serve as a flexible baseline for optical flow estimation.

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