UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, Jian Sun
Abstract
We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels. Moreover, we propose a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels. By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, includ-
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Install the CLIlune papers fulltext 8a74e352-ecad-42b7-a5c6-a859492dd4cfCited by top-tier papers31
- Learning Optical Flow from Continuous Spike StreamsRui Zhao, Ruiqin Xiong, Jing Zhao, Zhaofei Yu et al.NeurIPS 2022 · 49 citations
- GAFlow: Incorporating Gaussian Attention into Optical FlowAo Luo, Fan Yang, Xin Li, Lang Nie et al.ICCV 2023 · 35 citations
- Learning Pixel Trajectories with Multiscale Contrastive Random WalksZhangxing Bian, Allan Jabri, Alexei A. Efros, Andrew OwensCVPR 2022 · 35 citations
- IFOR: Iterative Flow Minimization for Robotic Object RearrangementAnkit Goyal, Arsalan Mousavian, Chris Paxton, Yu-Wei Chao et al.CVPR 2022 · 34 citations
- Learning Optical Flow from Event Camera with Rendered DatasetXinglong Luo, Kunming Luo, Ao Luo, Zhengning Wang et al.ICCV 2023 · 28 citations
Builds on2
- MaskFlownet: Asymmetric Feature Matching With Learnable Occlusion MaskShengyu Zhao, Yilun Sheng, Yue Dong, Eric I-Chao Chang et al.CVPR 2020
- Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow EstimationLiang Liu, Jiangning Zhang, Ruifei He, Yong Liu et al.CVPR 2020
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