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- W4386076238 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 crossattention 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." @default.
- W4386076238 created "2023-08-23" @default.
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- W4386076238 date "2023-06-01" @default.
- W4386076238 modified "2023-09-28" @default.
- W4386076238 title "TransFlow: Transformer as Flow Learner" @default.
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- W4386076238 doi "https://doi.org/10.1109/cvpr52729.2023.01732" @default.
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