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Publications about 'Artificial intelligence'
Articles in journal or book chapters
  1. 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]


  2. 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]


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, 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, 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]


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: Tue Jul 14 12:42:55 2020