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Devon: Deformable Volume Network for Learning Optical Flow

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Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr

Computer Vision and Pattern Recognition · Submitted 20 Feb 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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arXiv ID1802.07351 (version 2)
Pith IDJJ4X4HWN, Pith's permanent record of this paper and its review
CitationHeng Wang, Jack Valmadre, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr, Yao Lu. "Devon: Deformable Volume Network for Learning Optical Flow." arXiv:1802.07351 (2018). https://pith.science/paper/JJ4X4HWN
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,442 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.13 · human participants 0.02 · controlled comparison 0.46 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.94 · mentions code or data availability 0.03.

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arXiv listing abstract v2. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. background 1.00 0.05 0.03 asserted 1.00
s1 Despite their impressive results, it is known that there are two problems with the approach. background 0.82 0.06 0.02 asserted 0.99
s2 First, the multi-resolution estimation of optical flow fails in situations where small objects move fast. background 0.86 0.08 0.03 asserted 1.00
s3 Second, warping creates artifacts when occlusion or dis-occlusion happens. background 0.95 0.06 0.02 asserted 1.00
s4 In this paper, we propose a new neural network module, Deformable Cost Volume, which alleviates the two problems. contribution 0.99 0.90 0.22 asserted 0.98
s5 Based on this module, we designed the Deformable Volume Network (Devon) which can estimate multi-scale optical flow in a single high resolution. contribution 0.61 0.88 0.16 asserted 0.99
s6 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. result 0.99 0.95 0.82 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.99.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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.

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