Ghent University Academic Bibliography

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Multi-modal hit detection and positional analysis in padel competitions

Robbe Decorte (UGent) , Martin Paré, Jelle Vanhaeverbeke (UGent) , Joachim Taelman (UGent) , Maarten Slembrouck (UGent) and Steven Verstockt (UGent)

(2024) 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops p.3306-3314
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Label efficient lifelong multi-view broiler detection

Thorsten Cardoen (UGent) , Sam Leroux (UGent) and Pieter Simoens (UGent)

(2024) 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops p.5393-5402
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Multi-bit, black-box watermarking of deep neural networks in embedded applications

Sam Leroux (UGent) , Stijn Vanassche and Pieter Simoens (UGent)

(2024) 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops p.2121-2130
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Test-time specialization of dynamic neural networks

Sam Leroux (UGent) , D Katare, A.Y. Ding and Pieter Simoens (UGent)

(2024) 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops p.1048-1056
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Mitigating bias using model-agnostic data attribution

Sander De Coninck (UGent) , Sam Leroux (UGent) and Pieter Simoens (UGent)

(2024) 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops p.235-243

Academic Bibliography

Multi-modal hit detection and positional analysis in padel competitions

Label efficient lifelong multi-view broiler detection

Multi-bit, black-box watermarking of deep neural networks in embedded applications

Test-time specialization of dynamic neural networks

Mitigating bias using model-agnostic data attribution

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