Tomasz Trzciński, DSc, PhD

Assistant Professor, Warsaw University of Technology

Polski

Biography

Tomasz Trzciński is an Assistant Professor in the Division of Computer Graphics in the Institute of Computer Science at Warsaw University of Technology since 2015, where he leads a Computer Vision Lab. He obtained his D.Sc. degree (habilitation) in 2020 at Warsaw University of Technology and his Ph.D. in Computer Vision at École Polytechnique Fédérale de Lausanne in 2014. He received his M.Sc. degree in Research on Information and Communication Technologies from Universitat Politècnica de Catalunya and M.Sc. degree in Electronics Engineering from Politecnico di Torino in 2010. He is an Associate Editor of IEEE Access and frequently serves as a reviewer in major computer vision conferences (CVPR, ICCV, ECCV, ACCV, BMVC, ICML, MICCAI) and international journals (TPAMI, IJCV, CVIU, TIP, TMM). His professional appointments include work with Google in 2013, Qualcomm Corporate R&D in 2012 and Telefónica R&D in 2010. He was a Visiting Scholar at Stanford University in 2017 and at Nanyang Technological University in 2019. He is a co-organizer of warsaw.ai, a member of IEEE and Computer Vision Foundation, an expert of National Science Centre and Foundation for Polish Science as well as a member of Scientific Board for PLinML and Data Science Summit conferences. He is a Chief Scientist and Partner at Tooploox where he leads a team of machine learning researchers and engineers. He co-founded Comixify, a technology startup focused on using machine learning algorithms for editing videos.

Research interests: computer vision (SLAM, visual search), machine learning (deep learning, generative models, medical applications), representation learning (binary descriptors).

Contact details

address: ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
email: tomasz.trzcinski@pw.edu.pl
tel: +48 22 234 7650
office hours: online after email contact

Publications

Selected journals:

    • K. Deja, J. Dubinski, P. Nowak, S. Wenzel, P. Spurek, T. Trzcinski. End-to-end Sinkhorn Autoencoder with Noise Generator. IEEE Access, 2021. pdf
    • M. Zamorski, M. Zieba, P. Klukowski, R. Nowak, K. Kurach, W. Stokowiec, T. Trzcinski. Adversarial autoencoders for compact representations of 3D point clouds, Computer Vision and Image Understanding, 2020. arXiv
    • I. Tautkute, T. Trzcinski, A. Skorupa, L. Brocki, K. Marasek. DeepStyle: Multimodal Search Engine for Fashion and Interior Design. IEEE Access, Vol. 6, Nr. 1, p. 84613-84628, 2019. pdf
    • M. Pesko, A. Svystun, P. Andruszkiewicz, P. Rokita, T. Trzcinski. Comixify: Transform video into comics, Fundamenta Informaticae, Vol. 168, nr 2-4, p. 311-333, 2019. arXiv demo code
    • I. Tautkute, T. Trzcinski. Classifying and Visualizing Emotions with EmotionalDAN, Fundamenta Informaticae, Vol. 168, Nr. 2-4, p. 269-285, 2019. arXiv
    • M. Komorowski, T. Trzcinski. Random Binary Search Trees for approximate nearest neighbour search in binary spaces, Applied Soft Computing, Vol. 79, p. 87-93, 2019. official version
    • A. Bielski, T. Trzcinski. Understanding Multimodal Popularity Prediction of Social Media Videos with Self-Attention. IEEE Access, Vol. 6, Nr. 1, p. 74277-74287, 2018. pdf
    • T. Trzcinski, P. Rokita. Predicting popularity of online videos using Support Vector Regression. IEEE Trans. Multimedia (TMM). Vol. 19, Nr. 11, p. 2561-2570, 2017. arXiv
    • T. Trzcinski, M. Christoudias, V. Lepetit. Learning Image Descriptors with Boosting. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI). Vol. 37, Nr. 3, pp. 597-610, 2015. pdf
    • B. Fan, Q. Kong, T. Trzcinski, Z. Wang, C. Pan, P. Fua. Receptive Fields Selection for Binary Feature Description. IEEE Trans. Image Processing (TIP). Vol. 23, Nr. 6, pp. 2583-2595, 2014. official version
    • T. Trzcinski, V. Lepetit, P. Fua. Thick Boundaries in Binary Space and their Influence on Nearest-Neighbor Search. Pattern Recognition Letters (PRL). Vol. 33, pp. 2173-2180, 2012. pdf, code
    • M. Calonder, V. Lepetit, M. Ozuysal, T. Trzcinski, C. Strecha, P. Fua. BRIEF: Computing a local binary descriptor very fast. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI). Vol. 34, Nr. 7, pp. 1281 - 1298, 2012. pdf

    Selected conferences:

    • P. Spurek, S. Winczowski, J. Tabor, M. Zamorski, M. Zięba, T. Trzcinski. Hypernetwork approach to generating point clouds, International Conference on Machine Learning (ICML), 2020. arXiv
    • M. Koperski, T. Konopczyński, P. Semberecki, R. Nowak, T. Trzcinski. Plugin Networks for Inference under Partial Evidence, IEEE Workshop on Applications of Computer Vision (WACV), 2020. arXiv
    • W. Oleszkiewicz, S. Jastrzębski, T. Makino, T. Trzciński, L. Moy, K. Cho, L. Heacock, K. J. Geras. Understanding the robustness of deep neural network classifiers for breast cancer screening, International Conference on Learning Representations (ICLR), AI for Affordable Healthcare Workshop, 2020. arXiv
    • G. Kurzejamski, J. Komorowski, L. Dabala, K. Czarnota, S. Lynen, T. Trzcinski. SuperNCN: Neighbourhood consensus network for robust outdoor scenes matching, Advanced Concepts for Intelligent Vision Systems (ACIVS), 2020. arXiv
    • M. Zieba, P. Semberecki, T. El-Gaaly, T. Trzcinski. BinGAN: Learning Compact Binary Descriptors with a Regularized GAN. Neural Information Processing Systems (NeurIPS), 2018. arXiv
    • N. Kapinski, J. Zielinski, B. Borucki, T. Trzcinski, B. Ciszkowska-Lyson, K. Nowinski. Estimating Achilles tendon healing progress with convolutional neural networks. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018. arXiv
    • W. Oleszkiewicz, P. Kairouz, K. Piczak, R. Rajagopal, T. Trzcinski. Siamese Generative Adversarial Privatizer for Biometric Data. Asian Conference on Computer Vision (ACCV), 2018. arXiv
    • T. Trzcinski, J. Komorowski, L. Dabala, K. Czarnota, G. Kurzejamski, S. Lynen. SConE: Siamese Constellation Embedding Descriptor for Image Matching. European Conference on Computer Vision (ECCV), Workshop on 3D Reconstruction in the Wild, 2018. arXiv
    • J. Komorowski, K. Czarnota, T.Trzcinski, L. Dabala, S. Lynen. Interest point detectors stability evaluation on ApolloScape dataset. European Conference on Computer Vision (ECCV), Workshop on ApolloScape: Vision-based Navigation for Autonomous Driving, 2018. arXiv
    • A. Bielski, T. Trzcinski. Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism. Computer Vision and Pattern Recognition (CVPR), Understanding Subjective Attributes of Data Workshop, 2018 (oral presentation). arXiv
    • I. Tautkute, T. Trzcinski, A. Bielski. I Know How You Feel: Emotion Recognition with Facial Landmarks. Computer Vision and Pattern Recognition (CVPR), Women in Computer Vision Workshop, 2018. arXiv
    • T. Trzcinski, M. Glinka, Ł. Graczykowski for the ALICE Collaboration. Using Random Forest Classifier for particle identification in the ALICE Experiment. Conference on Information Technology, Systems Research and Computational Physics (ITSRCP), 2018. pdf
    • K. Deja, T. Trzcinski, Ł. Graczykowski for the ALICE Collaboration. Generative Models for Fast Cluster Simulations in the TPC for the ALICE Experiment. Conference on Information Technology, Systems Research and Computational Physics (ITSRCP), 2018. pdf
    • T. Trzcinski, A. Bielski, P. Cyrta, M. Zak. SocialML: machine learning for social media video creators. Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Creativity and Design, 2017. arXiv
    • M. Kowalski, J. Naruniec, T. Trzcinski. Deep Alignment Network: A convolutional neural network for robust face alignment. Computer Vision and Pattern Recognition (CVPR), Face Detection in the Wild Workshop, 2017. arXiv
    • T. Trzcinski, M. Christoudias, P. Fua, V. Lepetit. Boosting Binary Keypoint Descriptors. Computer Vision and Pattern Recognition (CVPR), 2013. pdf, code
    • T. Trzcinski, M. Christoudias, V. Lepetit, P. Fua. Learning Image Descriptors with the Boosting-Trick. Neural Information Processing Systems (NIPS), 2012. pdf
    • T. Trzcinski, V. Lepetit. Efficient Discriminative Projections for Compact Binary Descriptors. European Conference on Computer Vision (ECCV), 2012. pdf, code
    • D. Marimon, T. Adamek, A. Bonnin, T. Trzcinski. Enhancing global positioning by image recognition. International Symposium on Mixed and Augmented Reality (ISMAR), Workshop on Enabling Large-Scale Outdoor Mixed Reality and Augmented Reality, 2011. pdf

    Others:

    • T. Trzcinski. Learning and Matching Binary Local Feature Descriptors. Ph.D. Thesis, EPFL, n° 6226 (2014). pdf
    • T. Trzcinski. Towards Precise Outdoor Localisation Based on Image Recognition. M.Sc. Thesis, Universitat Politècnica de Catalunya, Politecnico di Torino (2010). pdf
    Google Scholar Profile

    Media coverage

    Funding

    • Microsoft Research PhD Scholarship Award: Low Shot Realistic Human Rendering from Partial Information, 2020-2023.
    • Grant of Priority Research Domain at WUT - Artificial Intelligence and Robotics: Binary representations and their application in continual learning, 2020-2021.
    • Grant of Priority Research Domain at WUT - High Energy Physics and Experimantal Techniques: WUT@ALICE: Study of fundamental properties of strongly interacting matter with particle correlations and machine learning in ALICE at LHC, 2020-2021.
    • Grant of Scientific Discipline of Computer Science and Telecommunications at WUT: Spontanuous preterm birth prediction based on ultrasound data using machine learning methods, 2020-2021.
    • FNP TEAM-NET (UJ): Bio-inspired artificial neural networks, 2019-2023. project website
    • Google Project ARCore: Hierarchical visual representations for visual localization, 2019-2020.
    • Dean's grant: Preterm birth prediction based on ultrasound images using artificial neural networks, 2019.
    • Google Project ARCore: Improving stability of keypoint detection using deep neural networks, 2018-2019.
    • Dean's grant: Online social media video classification with deep neural networks, 2017.
    • SONATA 11/ST6: The development of machine learning methods for big data quality monitoring and its interactive visualisation in the frames of the ALICE experiment at the Large Hadron Collider at CERN, 2016-2019.
    • Google Project Tango: Efficient and accurate nearest-neighbor search for binary local feature descriptors, 2016-2017.
    • Dean's grant: Application of artificial intelligence algorithms for the analysis of viral videos' phenomenon, 2015.

    Teaching

    • Introduction to Artificial Intelligence: PW, since 2017.
    • Digital Image Processing: PW, since 2015.
    • Algorithm Analysis: PW, since 2015.
    • Foundations of Imaging Science: EPFL, 2011-2013.
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