24 citations · 36 across the 5 of their papers we have counts for
5 papers
CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using…
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remain…
HeRAFC: Heuristic Resource Allocation and Optimization in MultiFog-Cloud Environment
Chinmaya Kumar Dehury, Bharadwaj Veeravalli, Satish Narayana Srirama
By bringing computing capacity from a remote cloud environment closer to the user, fog computing is introduced. As a result, users can access the services from more nearby computin…
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
Ziyuan Zhao, Andong Zhu, Zeng Zeng +2
While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especial…
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
Ziyuan Zhao, Jinxuan Hu, Zeng Zeng +4
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in cli…