1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2026
Learnable Instance Attention Filtering for Adaptive Detector Distillation
Chen Liu, Qizhen Lan, Zhicheng Ding +2
As deep vision models grow increasingly complex to achieve higher performance, deployment efficiency has become a critical concern. Knowledge distillation (KD) mitigates this issue…
cs.CV2026
From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflows
Qizhen Lan, Aaron Choi, Jun Ma +4
Deploying medical image segmentation models in routine clinical workflows is often constrained by on-premises infrastructure, where computational resources are fixed and cloud-base…
cs.CV2026★ 1 cited
ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentation
Qizhen Lan, Yu-Chun Hsu, Nida Saddaf Khan +1
Accurate 3D medical image segmentation is vital for diagnosis and treatment planning, but state-of-the-art models are often too large for clinics with limited computing resources.…