13.6.1.2 Crowdsourcing, Recognition, Analysis, Descriptions

Chapter Contents (Back)
Knowledge. Crowdsourcing.

Rodrigues, F.[Filipe], Pereira, F.C.[Francisco C.], Ribeiro, B.[Bernardete],
Learning from multiple annotators: Distinguishing good from random labelers,
PRL(34), No. 12, 1 September 2013, pp. 1428-1436.
Elsevier DOI 1306
Multiple annotators; Crowdsourcing; Latent variable models; Expectation-Maximization; Logistic Regression BibRef

Kleiman, Y.[Yanir], Goldberg, G.[George], Amsterdamer, Y.[Yael], Cohen-Or, D.[Daniel],
Toward semantic image similarity from crowdsourced clustering,
VC(32), No. 6-8, June 2016, pp. 1045-1055.
WWW Link. 1608
BibRef

Martinho-Corbishley, D.[Daniel], Nixon, M.S.[Mark S.], Carter, J.N.[John N.],
Analysing comparative soft biometrics from crowdsourced annotations,
IET-Bio(5), No. 4, 2016, pp. 276-283.
DOI Link 1612
BibRef
And:
Retrieving relative soft biometrics for semantic identification,
ICPR16(3067-3072)
IEEE DOI 1705
Biometrics (access control), Cameras, Machine learning, Neural networks, Semantics, Surveillance, Training BibRef

Martinho-Corbishley, D.[Daniel], Nixon, M.S.[Mark S.], Carter, J.N.[John N.],
Super-Fine Attributes with Crowd Prototyping,
PAMI(41), No. 6, June 2019, pp. 1486-1500.
IEEE DOI 1905
Prototypes, Visualization, Face, Surveillance, Face recognition, Attribute-based pedestrian re-identification, soft biometrics, PETA dataset BibRef

Maharjan, S., Zhang, Y., Gjessing, S.,
Optimal Incentive Design for Cloud-Enabled Multimedia Crowdsourcing,
MultMed(18), No. 12, December 2016, pp. 2470-2481.
IEEE DOI 1612
Cloud computing BibRef

Kovashka, A.[Adriana], Russakovsky, O.[Olga], Fei-Fei, L.[Li], Grauman, K.[Kristen],
Crowdsourcing in Computer Vision,
FTCGV(10), No. 3, 2016, pp. 177-243.
DOI Link 1612
Crowdsourcing. BibRef

Rodrigues, F.[Filipe], Lourenšo, M., Ribeiro, B.[Bernardete], Pereira, F.C.[Francisco C.],
Learning Supervised Topic Models for Classification and Regression from Crowds,
PAMI(39), No. 12, December 2017, pp. 2409-2422.
IEEE DOI 1711
Analytical models, Data models, Inference algorithms, Predictive models, Stochastic processes, Topic models, crowdsoucing. BibRef

Krishna, R.[Ranjay], Zhu, Y.[Yuke], Groth, O.[Oliver], Johnson, J.[Justin], Hata, K.[Kenji], Kravitz, J.[Joshua], Chen, S.[Stephanie], Kalantidis, Y.[Yannis], Li, L.J.[Li-Jia], Shamma, D.A.[David A.], Bernstein, M.S.[Michael S.], Fei-Fei, L.[Li],
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations,
IJCV(123), No. 1, May 2017, pp. 32-73.
Springer DOI 1705
BibRef

Kumar, G.[Gautam], Narducci, F.[Fabio], Bakshi, S.[Sambit],
Knowledge Transfer and Crowdsourcing in Cyber-Physical-Social Systems,
PRL(164), 2022, pp. 210-215.
Elsevier DOI 2212
Cyber-physical-social systems, IoT, Crowdsourcing, Knowledge transfer BibRef

Zhang, J.[Jing], Xu, S.[Sunyue], Sheng, V.S.[Victor S.],
Crowdmeta: Crowdsourcing truth inference with meta-Knowledge transfer,
PR(140), 2023, pp. 109525.
Elsevier DOI 2305
Crowdsourcing, Truth inference, Transfer learning, Meta learning BibRef


Horn, G.V., Branson, S., Loarie, S., Belongie, S., Perona, P.,
Lean Multiclass Crowdsourcing,
CVPR18(2714-2723)
IEEE DOI 1812
Task analysis, Computational modeling, Crowdsourcing, Taxonomy, Predictive models, Birds BibRef

Kim, K.H., Aodha, O.M., Perona, P.,
Context Embedding Networks,
CVPR18(8679-8687)
IEEE DOI 1812
Visualization, Context modeling, Feature extraction, Training, Noise measurement, Data models, Crowdsourcing BibRef

Zhuang, B.[Bohan], Liu, L.Q.[Ling-Qiao], Li, Y.[Yao], Shen, C.H.[Chun-Hua], Reid, I.D.[Ian D.],
Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning from Web Data,
CVPR17(2915-2924)
IEEE DOI 1711
Crowdsource. Convolution, Feature extraction, Machine learning, Noise measurement, Robustness, Training, Visualization BibRef

Sharmanska, V., Hernandez-Lobato, D., Hernandez-Lobato, J.M., Quadrianto, N.,
Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations,
CVPR16(2194-2202)
IEEE DOI 1612
BibRef

Nicholson, B.[Bryce], Sheng, V.S.[Victor S.], Zhang, J.[Jing],
Noise correction of image labeling in crowdsourcing,
ICIP15(1458-1462)
IEEE DOI 1512
BibRef

Raykar, V.C., Yu, S.P.[Shi-Peng],
An Entropic Score to Rank Annotators for Crowdsourced Labeling Tasks,
NCVPRIPG11(29-32).
IEEE DOI 1205
BibRef

Welinder, P.[Peter], Perona, P.[Pietro],
Online crowdsourcing: Rating annotators and obtaining cost-effective labels,
ACVHL10(25-32).
IEEE DOI 1006
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
ACRONYM and SUCCESSOR Papers - Stanford University and Others .


Last update:Jun 1, 2023 at 10:05:03