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
    2023 MacDonald, L. E., Valmadre, J., Saratchandran, H., & Lucey, S. (2023). On skip connections and normalisation layers in deep optimisation. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems Vol. 36.
    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..
    Scopus3
    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 Scopus26 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 Scopus25 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 Scopus28 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 Scopus1413 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 Scopus3023 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 Scopus1786 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 Scopus331
    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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