Jack Valmadre

Dr Jack Valmadre

AIML Future Making Fellow

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

  • Journals
    Year Citation
    2023 MacDonald, L. E., Valmadre, J., & Lucey, S. (2023). On progressive sharpening, flat minima and generalisation.
    2017 Simon, T., Valmadre, J., Matthews, I., & Sheikh, Y. (2017). Kronecker-Markov Prior for Dynamic 3D Reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2201-2214.
    DOI Scopus13 WoS12 Europe PMC1

  • Conference Papers
    Year Citation
    2022 Valmadre, J. (2022). Hierarchical classification at multiple operating points. In S. Koyejo, S. Mohamed, A. Agarwal, A. Oh, D. Belgrave, & K. Cho (Eds.), Advances in Neural Information Processing Systems Vol. 35 (pp. 18034-18045). Online: Curran Associates, Inc..
    Scopus1
    2022 Iscen, A., Valmadre, J., Arnab, A., & Schmid, C. (2022). Learning with Neighbor Consistency for Noisy Labels. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Vol. 2022-June (pp. 4662-4671). Online: IEEE.
    DOI Scopus20 WoS3
    2020 Lu, Y., Valmadre, J., Wang, H., Kannala, J., Harandi, M., & Torr, P. H. S. (2020). Devon: Deformable volume network for learning optical flow. In Proceedings 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 2694-2702). Piscataway, New Jersey, USA: IEEE.
    DOI Scopus24 WoS2
    2019 Lu, Y., Valmadre, J., Wang, H., Kannala, J., Harandi, M., & Torr, P. H. S. (2019). Devon: Deformable volume network for learning optical flow. In Computer Vision – ECCV 2018 Workshops. ECCV 2018, Proceedings, Part VI Vol. 11134 LNCS (pp. 673-677). Switzerland: Springer International Publishing.
    DOI Scopus1
    2018 Valmadre, J., Bertinetto, L., Henriques, J. F., Tao, R., Vedaldi, A., Smeulders, A. W. M., . . . Gavves, E. (2018). Long-Term Tracking in the Wild: A Benchmark. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11207 LNCS (pp. 692-707). Switzerland: Springer International Publishing.
    DOI Scopus26 WoS74
    2017 Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., & Torr, P. H. S. (2017). End-to-end representation learning for Correlation Filter based tracking. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 Vol. 2017-January (pp. 5000-5008). online: IEEE.
    DOI Scopus1390 WoS1103
    2016 Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016). Fully-convolutional siamese networks for object tracking. In Computer Vision – ECCV 2016 Workshops. ECCV 2016 Proceedings, Part II Vol. 9914 LNCS (pp. 850-865). Switzerland: Springer International Publishing.
    DOI Scopus2934 WoS2444
    2016 Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., & Torr, P. H. S. (2016). Staple: Complementary learners for real-time tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 1401-1409). online: IEEE.
    DOI Scopus1759 WoS1316
    2016 Bertinetto, L., Henriques, J. F., Valmadre, J., Torr, P. H. S., & Vedaldi, A. (2016). Learning feed-forward one-shot learners. In Advances in Neural Information Processing Systems (pp. 523-531). Barcelona, Spain: ACM Association for Computing Machinery.
    DOI Scopus325
    2015 Bristow, H., Valmadre, J., & Lucey, S. (2015). Dense semantic correspondence where every pixel is a classifier. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 4024-4031). Santiago, CHILE: IEEE.
    DOI Scopus45 WoS19
    2015 Valmadre, J., Sridharan, S., Denman, S., Fookes, C., & Lucey, S. (2015). Closed-Form Solutions for Low-Rank Non-Rigid Reconstruction. In 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015 (pp. 252-257). Adelaide, AUSTRALIA: IEEE.
    DOI Scopus5
    2015 Valmadre, J., Sridharan, S., & Lucey, S. (2015). Learning detectors quickly with stationary statistics. In D. Cremers, I. Reid, H. Saito, & M. H. Yang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9003 (pp. 99-114). Singapore, SINGAPORE: SPRINGER-VERLAG BERLIN.
    DOI Scopus1
    2014 Simon, T., Valmadre, J., Matthews, I., & Sheikh, Y. (2014). Separable spatiotemporal priors for convex reconstruction of time-varying 3D point clouds. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8691 LNCS (pp. 204-219). Zurich, SWITZERLAND: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus21 WoS19
    2012 Zhu, Y., Valmadre, J., & Lucey, S. (2012). Camera-less articulated trajectory reconstruction. In Proceedings - International Conference on Pattern Recognition (pp. 841-844). Univ Tsukuba, Tsukuba, JAPAN: IEEE.
    Scopus5 WoS4
    2012 Valmadre, J., Zhu, Y., Sridharan, S., & Lucey, S. (2012). Efficient articulated trajectory reconstruction using dynamic programming and filters. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds.), Computer Vision - ECCV 2012: 12th European Conference on Computer Vision. Proceedings, Part 1 Vol. 7572 LNCS (pp. 72-85). Florence, Italy: Springer-Verlag.
    DOI Scopus14 WoS12
    2012 Valmadre, J., & Lucey, S. (2012). General trajectory prior for Non-Rigid reconstruction. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1394-1401). Providence, RI: IEEE.
    DOI Scopus50 WoS27
    2011 Valmadre, J., Upcroft, B., Sridharan, S., & Lucey, S. (2011). Graph rigidity for near-coplanar structure from motion. In Proceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 (pp. 480-486). IEEE.
    DOI
    2010 Valmadre, J., & Lucey, S. (2010). Deterministic 3D human pose estimation using rigid structure. In K. Daniilidis, P. Maragos, & N. Paragios (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 6313 LNCS (pp. 467-480). Heraklion, GREECE: SPRINGER-VERLAG BERLIN.
    DOI Scopus22 WoS19

  • Internet Publications
    Year Citation
    2021 Valmadre, J., Bewley, A., Huang, J., Sun, C., Sminchisescu, C., & Schmid, C. (2021). Local Metrics for Multi-Object Tracking.
    2014 Valmadre, J., Sridharan, S., & Lucey, S. (2014). Learning detectors quickly using structured covariance matrices.

  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2023 Co-Supervisor Data-centric Approaches and New Benchmarks for Few-Shot Learning Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Mr Raphael Jean-Marc Lafargue
    2023 Co-Supervisor Secrets of Implicit Neural Representation Doctor of Philosophy Doctorate Full Time Mr Yiping Ji
  • Position: AIML Future Making Fellow
  • Phone: 83130377
  • Email: jack.valmadre@adelaide.edu.au
  • Campus: Lot 14
  • Building: Australian Institute for Machine Learning Building, floor Ground Floor
  • Room: G.08
  • Org Unit: Australian Institute for Machine Learning - Projects
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