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Published in Multimedia Tools and Applications, 2023
A dataset and analysis pipeline for tampered HEVC-encoded videos to support forensic investigation and benchmarking.
Recommended citation: Singla, N., Singh, J., Nagpal, S., & Tokas, B. (2023). HEVC based tampered video database development for forensic investigation. Multimedia Tools and Applications, 1–34.
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Published in Scientific Data, 2025
A data descriptor introducing a novel crop type dataset collected using helmet-mounted cameras and labeled using deep learning methods to support agricultural research in Kenya.
Recommended citation: Nakalembe, C., Zvonkov, I., Kerner, H., Frimpong, D., Mwangi, K., Kioko, J., Tokas, B., Jawanjal, K., Smith, I., Paliyam, A., & others (2025). Helmets Labeling Crops: Kenya Crop Type Dataset Created via Helmet-Mounted Cameras and Deep Learning. Scientific Data, 12(1), 1496.
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Published in CVPR, 2025
An analysis showing how classification decisions contribute to geographic biases in street scene segmentation models and recommendations to mitigate them.
Recommended citation: Nair, R., Tokas, B., Tseng, G., Rolf, E., & Kerner, H. (2025). Classification Drives Geographic Bias in Street Scene Segmentation. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 629–638).
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Published in NeurIPS, 2025
Most ML datasets today contain biases. When we train models on these datasets, they often not only learn these biases but can worsen them — a phenomenon known as bias amplification. We propose a better metric to measure bias amplification.
Recommended citation: TBA
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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