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Publications about 'Learning'
Thesis
  1. A. Cioppa. Real-time semantics in video sequences. PhD thesis, University of Liège, Belgium, December 2020. Keyword(s): Sport, Soccer, Football, Machine learning, Deep learning, Semantics, Video understanding, Real-time, Semantic segmentation, Action spotting, Dataset, Background subtraction, Online distillation, SoccerNet. [bibtex-entry]


Articles in journal or book chapters
  1. A. Deliège, A. Cioppa, S. Giancola, M. Seikavandi, J. Dueholm, K. Nasrollahi, B. Ghanem, T. Moeslund, and M. Van Droogenbroeck. SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos. ArXiv, abs/2011.13367, November 2020. Keyword(s): SoccerNet, Dataset, Training data, Soccer, Football, Classification, Annotation, Neural network, Deep learning, Machine learning, Artificial intelligence, DeepSport. [bibtex-entry]


  2. A. Deliège, A. Cioppa, and M. Van Droogenbroeck. Ghost Loss to Question the Reliability of Training Data. IEEE Access, 8:44774-44782, March 2020. Keyword(s): Deep learning, Machine learning, Ghost, Artificial Intelligence, Training data, DeepSport. [bibtex-entry]


  3. A. Deliège, A. Cioppa, and M. Van Droogenbroeck. HitNet: a neural network with capsules embedded in a Hit-or-Miss layer, extended with hybrid data augmentation and ghost capsules. ArXiv, abs/1806.06519, June 2018. Keyword(s): Neural network, Deep learning, Machine learning, Artificial intelligence, Hit-or-Miss layer, Capsule, Ghost capsule, NMISTi, DeepSport. [bibtex-entry]


  4. C. Gomez Gonzalez, O. Absil, and M. Van Droogenbroeck. Supervised detection of exoplanets in high-contrast imaging sequences. Astronomy & Astrophysics, 613:1-13, May 2018. Keyword(s): Deep learning, SODINN, VORTEX project, High angular resolution, Image processing, Detection of planets, Sparse decomposition, Detection, Planets and satellites. [bibtex-entry]


  5. Q. Massoz, J. Verly, and M. Van Droogenbroeck. Multi-timescale drowsiness characterization based on a video of a driver's face. Sensors, 18(9):1-17, August 2018. Keyword(s): Drowsiness, Driver monitoring, Face, Eye closure, Pyschomotor vigilance test, Reaction time, CNN, Deep learning. [bibtex-entry]


  6. S. Piérard, S. Azrour, R. Phan-Ba, V. Delvaux, P. Maquet, and M. Van Droogenbroeck. Diagnosing multiple sclerosis with a gait measuring system, an analysis of the motor fatigue, and machine learning. Multiple Sclerosis Journal, 20(S1):171, September 2014. Note: Proceedings of ACTRIMS/ECTRIMS 2014 (Boston, USA), P232. Keyword(s): GAIMS, Multiple Sclerosis, Gait, Motor Fatigue. [bibtex-entry]


  7. S. Piérard, S. Azrour, R. Phan-Ba, and M. Van Droogenbroeck. GAIMS: A Reliable Non-Intrusive Gait Measuring System. ERCIM News, 95:26-27, October 2013. Keyword(s): Multiple Sclerosis, Gait, Outcome measure, GAIMS, Immersion, Machine learning. [bibtex-entry]


Conference articles
  1. A. Cioppa, A. Deliège, S. Giancola, B. Ghanem, M. Van Droogenbroeck, R. Gade, and T. Moeslund. A Context-Aware Loss Function for Action Spotting in Soccer Videos. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, USA, pages 13123-13133, June 2020. Keyword(s): Action spotting, Artificial Intelligence, Soccer, Deep learning, Loss, DeepSport. [bibtex-entry]


  2. A. Cioppa, A. Deliège, N. Ul Huda, R. Gade, M. Van Droogenbroeck, and T. Moeslund. Multimodal and multiview distillation for real-time player detection on a football field. In IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), CVsports, Seattle, Washington, USA, pages 3846-3855, June 2020. Note: Best CVSports paper award. Keyword(s): Soccer, Football, Artificial intelligence, Deep learning, Machine learning, Multiview distillation, Multimodal distillation, Knowledge distillation, Player detection, Thermal camera, Fisheye camera, Real-time, DeepSport, Distillation, ViBe. [bibtex-entry]


  3. A. Cioppa, A. Deliège, M. Istasse, C. De Vlesschouwer, and M. Van Droogenbroeck. ARTHuS: Adaptive Real-Time Human Segmentation in Sports through Online Distillation. In IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), CVsports, Long Beach, California, USA, pages 2505-2514, June 2019. Note: Best CVSports paper award. Keyword(s): Soccer, Semantic segmentation, Artificial intelligence, Machine learning, Deep learning, CNN, DeepSport. [bibtex-entry]


  4. A. Deliège, A. Cioppa, and M. Van Droogenbroeck. An Effective Hit-or-Miss Layer Favoring Feature Interpretation as Learned Prototypes Deformations. In AAAI Conference on Artificial Intelligence, Workshop on Network Interpretability for Deep Learning, Honolulu, Hawaii, USA, pages 1-8, January 2019. Keyword(s): Deep learning, Machine learning, Artificial intelligence, Neural network, Classification, Capsule, HitNet, Hit-or-miss layer, Feature interpretation, Centripetal loss, Prototype, DeepSport. [bibtex-entry]


  5. A. Deliège, A. Kumar, M. Istasse, C. De Vlesschouwer, and M. Van Droogenbroeck. Ordinal Pooling. In British Machine Vision Conference (BMVC), Cardiff, Wales, pages 1-12, September 2019. Keyword(s): Deep learning, Machine learning, Pooling, Artificial intelligence, Neural network, Convolutional layer, CNN, DeepSport. [bibtex-entry]


  6. M. Fonder and M. Van Droogenbroeck. Mid-Air: A multi-modal dataset for extremely low altitude drone flights. In IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), UAVision, Long Beach, California, USA, pages 553-562, June 2019. Keyword(s): UAV, Dataset, Mid-Air, Positioning, Benchmark, Drone, Artificial Intelligence, Deep learning. [bibtex-entry]


  7. A. Cioppa, A. Deliège, and M. Van Droogenbroeck. A bottom-up approach based on semantics for the interpretation of the main camera stream in soccer games. In IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), CVsports, Salt Lake City, Utah, USA, pages 1846-1855, June 2018. Note: Best CVSports paper award. Keyword(s): Soccer, Semantic segmentation, Artificial intelligence, Deep learning, Machine learning, CNN, DeepSport. [bibtex-entry]


  8. S. Azrour, S. Piérard, P. Geurts, and M. Van Droogenbroeck. A two-step methodology for human pose estimation increasing the accuracy and reducing the amount of learning samples dramatically. In Advanced Concepts for Intelligent Vision Systems (ACIVS), volume 10617 of Lecture Notes in Computer Science, pages 3-14, 2017. Keyword(s): Pose estimation, Orientation estimation. [bibtex-entry]


  9. M. Braham, S. Piérard, and M. Van Droogenbroeck. Semantic Background Subtraction. In IEEE International Conference on Image Processing, Beijing, China, pages 4552-4556, September 2017. Keyword(s): Background subtraction, Change detection, Semantic segmentation, Scene labeling, Scene parsing, Classification, Machine learning, Deep learning, CDNet 2014. [bibtex-entry]


  10. S. Azrour, S. Piérard, and M. Van Droogenbroeck. Leveraging orientation knowledge to enhance human pose estimation methods. In Articulated Motion and Deformable Objects AMDO, volume 9756 of Lecture Notes in Computer Science, Palma, Mallorca, Spain, pages 81-87, 2016. Springer. Keyword(s): Human pose estimation, Orientation, 3D, Machine learning. [bibtex-entry]


  11. M. Braham and M. Van Droogenbroeck. Deep Background Subtraction with Scene-Specific Convolutional Neural Networks. In International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia, pages 1-4, May 2016. Keyword(s): Background subtraction, Deep learning, Machine learning, CDNet, Change detection, CDNet 2014. [bibtex-entry]


  12. S. Azrour, S. Piérard, P. Geurts, and M. Van Droogenbroeck. Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, pages 649-654, April 2014. Keyword(s): Data normalization, Machine learning, Multiple sclerosis. [bibtex-entry]


  13. S. Piérard, A. Alvarez, A. Lejeune, and M. Van Droogenbroeck. On-the-fly Domain Adaptation of Binary Classifiers. In Belgian-Dutch Conference on Machine Learning (BENELEARN), Brussels, Belgium, June 2014. Keyword(s): Classifier, Machine learning. [bibtex-entry]


  14. S. Piérard, R. Phan-Ba, and M. Van Droogenbroeck. Machine learning techniques to assess the performance of a gait analysis system. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, pages 419-424, April 2014. Keyword(s): GAIMS, Feet, Machine learning. [bibtex-entry]


  15. S. Piérard and M. Van Droogenbroeck. Estimation of human orientation based on silhouettes and machine learning principles. In International Conference on Pattern Recognition Applications and Methods (ICPRAM), volume 2, Vilamoura, Portugal, pages 51-60, February 2012. Keyword(s): Human, Orientation, Machine learning, Regression, Estimation, MakeHuman. [bibtex-entry]


  16. S. Piérard, D. Leroy, J.-F. Hansen, and M. Van Droogenbroeck. Estimation of human orientation in images captured with a range camera. In Advanced Concepts for Intelligent Vision Systems (ACIVS), volume 6915 of Lecture Notes in Computer Science, pages 519-530, 2011. Springer. Keyword(s): Human, Orientation, Depth, Range camera, Machine learning, MakeHuman. [bibtex-entry]


  17. O. Barnich, S. Piérard, and M. Van Droogenbroeck. A virtual curtain for the detection of humans and access control. In Advanced Concepts for Intelligent Vision Systems (ACIVS 2010), Part II, Sydney, Australia, pages 98-109, December 2010. Keyword(s): Detection, Human, Access control, Curtain, Silhouette, Laser, Sensor, Machine learning, Training, Door. [bibtex-entry]


  18. E. Van Tomme, M. Van Droogenbroeck, and P. Debie. Narrowband versus broadband for tele-education : a critical evaluation. In International Conference on Technology Supported Learning, Berlin, pages 200-207, November 1996. Online Educa Berlin. Keyword(s): Telecommunications, Learning. [bibtex-entry]


Miscellaneous
  1. M. Van Droogenbroeck, M. Braham, and A. Cioppa. Foreground and background detection method. United States Patent and Trademark Office, US 10,706,558 B2, 26 pages, July 2020. Keyword(s): Background subtraction, Semantic segmentation, Patent, Machine learning, Deep learning, Artificial intelligence, DeepSport. [bibtex-entry]


  2. M. Van Droogenbroeck, M. Braham, and S. Piérard. Foreground and background detection method. European Patent Office, EP 3438929 B2, July 2020. Keyword(s): Background subtraction, Semantic segmentation, Patent, Machine learning, Deep learning, Artificial intelligence. [bibtex-entry]


  3. M. Van Droogenbroeck, M. Braham, and S. Piérard. Foreground and background detection method. United States Patent and Trademark Office, US 10614736 B2, 16 pages, April 2020. Keyword(s): Background subtraction, Semantic segmentation, Patent, Machine learning, Deep learning, Artificial intelligence. [bibtex-entry]


  4. M. Van Droogenbroeck, M. Braham, and S. Piérard. Foreground and background detection method. Chinese Patent Office, CN109389618A, February 2019. Keyword(s): Background subtraction, Semantic segmentation, Patent, Machine learning, Deep learning, Artificial intelligence. [bibtex-entry]


  5. M. Van Droogenbroeck, A. Deliège, and A. Cioppa. Image classification using neural networks. World Intellectual Property Organization, WO 2019/238976, 55 pages, December 2019. Keyword(s): Patent, Machine learning, Deep learning, Artificial intelligence, HitNet, Capsule, Data augmentation, DeepSport. [bibtex-entry]


  6. M. Van Droogenbroeck, A. Deliège, and A. Cioppa. Image classification using neural networks. European Patent Office, EP 3 582 142 A1, 32 pages, June 2018. Keyword(s): Patent, Machine learning, Deep learning, Artificial intelligence, HitNet, Capsule, Data augmentation, DeepSport. [bibtex-entry]



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Last modified: Wed Jan 6 10:30:33 2021