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• W2789005540 abstract "State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions. Despite their impressive results, it is known that there are two problems with the approach. First, the multi-resolution estimation of optical flow fails in situations where small objects move fast. Second, warping creates artifacts when occlusion or dis-occlusion happens. In this paper, we propose a new neural network module, Deformable Cost Volume, which alleviates the two problems. Based on this module, we designed the Deformable Volume Network (Devon) which can estimate multi-scale optical flow in a single high resolution. Experiments show Devon is more suitable in handling small objects moving fast and achieves comparable results to the state-of-the-art methods in public benchmarks." @default.
• W2789005540 created "2018-03-06" @default.
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• W2789005540 date "2018-02-20" @default.
• W2789005540 modified "2023-09-22" @default.
• W2789005540 title "Devon: Deformable Volume Network for Learning Optical Flow" @default.
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• W2789005540 hasPublicationYear "2018" @default.
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