Supplementary Material 1/2 Learning a Conﬁdence Measure for...

Supplementary Material 1/2Learning a Confidence Measure for Optical

FlowOisin Mac Aodha, Ahmad Humayun, Marc Pollefeys and Gabriel J. Brostow

Abstract—In this supplementary material we provide the complete comparison to the results of other methods as presented inFigure 4 in the main paper for 32 sequences. The results are quantified in Table 1. Our results are presented with ϵepe = 0.25.For each sequence we show the result of comparing our confidence measure against six others for four different optical flowalgorithms (TV, FL, CN and LD).

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 1 Venus

strEv3strCtstrCsstrCcgradpValusopt

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 1 Venus

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 1 Venus

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 1 Venus

Fig. 1: 1 Venus

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 2 Urban3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 2 Urban3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 2 Urban3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 2 Urban3

Fig. 2: 2 Urban3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 3 Urban2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 3 Urban2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 3 Urban2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 3 Urban2

Fig. 3: 3 Urban2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 4 RubberWhale

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 4 RubberWhale

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 4 RubberWhale

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 4 RubberWhale

Fig. 4: 4 RubberWhale

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 5 Hydrangea

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 5 Hydrangea

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 5 Hydrangea

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 5 Hydrangea

Fig. 5: 5 Hydrangea

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 6 Grove3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 6 Grove3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 6 Grove3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 6 Grove3

Fig. 6: 6 Grove3

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 7 Grove2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 7 Grove2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 7 Grove2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 7 Grove2

Fig. 7: 7 Grove2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 8 Dimetrodon

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 8 Dimetrodon

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 8 Dimetrodon

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 8 Dimetrodon

Fig. 8: 8 Dimetrodon

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 9 Crates1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 9 Crates1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 9 Crates1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 9 Crates1

Fig. 9: 9 Crates1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 10 Crates2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 10 Crates2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 10 Crates2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 10 Crates2

Fig. 10: 10 Crates2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 13 Mayan1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 13 Mayan1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 13 Mayan1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 13 Mayan1

Fig. 11: 13 Mayan1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 14 Mayan2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 14 Mayan2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 14 Mayan2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 14 Mayan2

Fig. 12: 14 Mayan2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 15 YosemiteSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 15 YosemiteSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 15 YosemiteSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 15 YosemiteSun

Fig. 13: 15 YosemiteSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 16 GroveSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 16 GroveSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 16 GroveSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 16 GroveSun

Fig. 14: 16 GroveSun

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 17 Robot

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 17 Robot

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 17 Robot

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 17 Robot

Fig. 15: 17 Robot

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 18 Sponza1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 18 Sponza1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 18 Sponza1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 18 Sponza1

Fig. 16: 18 Sponza1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 19 Sponza2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 19 Sponza2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 19 Sponza2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 19 Sponza2

Fig. 17: 19 Sponza2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 22 Crates1Htxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 22 Crates1Htxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 22 Crates1Htxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 22 Crates1Htxtr2

Fig. 18: 22 Crates1Htxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 24 Crates2Htxtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 24 Crates2Htxtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 24 Crates2Htxtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 24 Crates2Htxtr1

Fig. 19: 24 Crates2Htxtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 26 Brickbox1t1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 26 Brickbox1t1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 26 Brickbox1t1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 26 Brickbox1t1

Fig. 20: 26 Brickbox1t1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 29 Brickbox2t2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 29 Brickbox2t2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 29 Brickbox2t2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 29 Brickbox2t2

Fig. 21: 29 Brickbox2t2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 30 GrassSky0

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 30 GrassSky0

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 30 GrassSky0

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 30 GrassSky0

Fig. 22: 30 GrassSky0

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 39 GrassSky9

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 39 GrassSky9

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 39 GrassSky9

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 39 GrassSky9

Fig. 23: 39 GrassSky9

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 49 TxtRMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 49 TxtRMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 49 TxtRMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 49 TxtRMovement

Fig. 24: 49 TxtRMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 50 TextLMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 50 TextLMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 50 TextLMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 50 TextLMovement

Fig. 25: 50 TextLMovement

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 51 blow1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 51 blow1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 51 blow1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 51 blow1Txtr1

Fig. 26: 51 blow1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 88 blow19Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 88 blow19Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 88 blow19Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 88 blow19Txtr2

Fig. 27: 88 blow19Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 89 drop1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 89 drop1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 89 drop1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 89 drop1Txtr1

Fig. 28: 89 drop1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 106 drop9Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 106 drop9Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 106 drop9Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 106 drop9Txtr2

Fig. 29: 106 drop9Txtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 107 roll1txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 107 roll1txtr1

0 10 20 30 40 50 60 70 80 90 1000

x 10−3

% Pixels

CN − 107 roll1txtr1

0 10 20 30 40 50 60 70 80 90 1000

x 10−3

% Pixels

LD − 107 roll1txtr1

Fig. 30: 107 roll1txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 124 rollTxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 124 rollTxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 124 rollTxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 124 rollTxtr2

Fig. 31: 124 rollTxtr2

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

TV − 125 street1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

FL − 125 street1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

CN − 125 street1Txtr1

0 10 20 30 40 50 60 70 80 90 1000

% Pixels

LD − 125 street1Txtr1

Fig. 32: 125 street1Txtr1

Supplementary Material 1/2 Learning a Conﬁdence Measure for...

Documents

Transcript of Supplementary Material 1/2 Learning a Conﬁdence Measure for...

Supplementary Material: The Edge of Depth: Explicit Constraints between Segmentation and Depthcvlab.cse.msu.edu/pdfs/zhu_brazil_liu_cvpr2020_supp.pdf · Supplementary Material: The

1 Appendix A. Supplementary Material for McCave, Thornalley and …eprints.esc.cam.ac.uk/4044/2/mmc1 (2).pdf · 2017. 8. 22. · 1 Appendix A. Supplementary Material for McCave, Thornalley

Supplementary Material: Unsupervised Learning of Probably ...openaccess.thecvf.com/content_CVPR_2020/...C. V. Jawahar. Cats and dogs. In Proc. CVPR, 2012.1 [8] Yuxin Wu and Kaiming

Supplementary material Litzinger et al

Supplementary Material for the Manuscript...Supplementary Material for the Manuscript A novel multi-modal RAGE-specific inhibitor controls amyloid-β-mediated brain disorder in mice

SUPPLEMENTARY MATERIAL Supplementary Experimental Procedures · SUPPLEMENTARY MATERIAL Supplementary Experimental Procedures IFNα/STAT2 array design. Aiming at designing an oligonucleotide-based

Supplementary Material: Pixelwise View Selection for ...

Machine Learning Probabilistic Machine Learning · Machine Learning Probabilistic Machine Learning learning as inference, Bayesian Kernel Ridge regression = Gaussian Processes, Bayesian

Supplementary Material for Stress Gradient Plasticity - PNAS€¦ · Supplementary Material for Stress Gradient Plasticity ... our simulations and also show examples of the actual

Difuryl(supermesityl)borane: a versatile building block for ...Electronic Supplementary Material for Difuryl(supermesityl)borane: a versatile building block for extended π-conjugated

Supplementary Material of Anisotropic isolation by ... · Supplementary Material of "Anisotropic isolation by distance: ... QEC í í í í í ... Balochi Br ahui Bu r usho Cambodian

link. · Web viewElectronic Supplementary Material α-Hemolysin nanopore studies reveal strong interactions between biogenic polyamines and DNA hairpins Yun Ding, Aaron M. Fleming,

Supplementary Material Synthesis and Characterisation of ...

N-Heterocyclic carbene catalysed β-lactam synthesis · Supplementary Material for Organic & Biomolecular Chemistry ... N-Heterocyclic carbene catalysed ... II. Synthesis of ketenes

Electronic Supplementary Material (ESI) for Journal of ... · Anna Dobrowolska, dKatarzyna Czaczyk, Roberta Galli, e Allison L. Stelling, f Thomas Behm, c Łukasz 5 Klapiszewski,

danielafrost.files.wordpress.com · Web view2020. 1. 13. · Supplementary Material for: Upper mantle slab under Alaska: contribution to anomalous core-phase observations on south-Sandwich

Supplementary materials Supplementary methods 1. Transfection efficiency

Analytical and Bioanalytical Chemistry Electronic ...10.1007/s00216-014-7819... · Analytical and Bioanalytical Chemistry Electronic Supplementary Material ... 59729-33-8 Citalopram

Reinforcement Learning - 4. Model-free reinforcement Learning

Supplementary Material Synthesis and Biological Screening of … · S1 10.1071/CH15527_AC The Authors 2016 Australian Journal of Chemistry 2016, 69(6), 662-671 Supplementary Material