3 citations · 3 across the 4 of their papers we have counts for
4 papers
Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception
Moseli Mots'oehli, Feimei Chen, Hok Wai Chan +4
The scarcity of autonomous vehicle datasets from developing regions, particularly across Africa's diverse urban, rural, and unpaved roads, remains a key obstacle to robust percepti…
Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise
Moseli Mots'oehli, Hope Mogale, Kyungim Baek
Fine-tuning pre-trained convolutional neural networks on ImageNet for downstream tasks is well-established. Still, the impact of model size on the performance of vision transformer…
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise
Moseli Mots'oehli, kyungim Baek
Active learning aims to train accurate classifiers while minimizing labeling costs by strategically selecting informative samples for annotation. This study focuses on image classi…
Deep Active Learning in the Presence of Label Noise: A Survey
Moseli Mots'oehli, Kyungim Baek
Deep active learning has emerged as a powerful tool for training deep learning models within a predefined labeling budget. These models have achieved performances comparable to tho…