8 citations · 16 across the 6 of their papers we have counts for
7 papers · 1 filter
Visual Detector Compression via Location-Aware Discriminant Analysis
Qizhen Lan, Jung Im Choi, Qing Tian
Deep neural networks are powerful, yet their high complexity greatly limits their potential to be deployed on billions of resource-constrained edge devices. Pruning is a crucial ne…
ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation
Qizhen Lan, Qing Tian
Dense visual prediction tasks, such as detection and segmentation, are crucial for time-critical applications (e.g., autonomous driving and video surveillance). While deep models a…
CLoCKDistill: Consistent Location-and-Context-aware Knowledge Distillation for DETRs
Qizhen Lan, Qing Tian
Object detection has advanced significantly with Detection Transformers (DETRs). However, these models are computationally demanding, posing challenges for deployment in resource-c…
Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation
Wentao Wang, Xi Xiao, Mingjie Liu +5
The accurate segmentation of medical images is crucial for diagnosing and treating diseases. Recent studies demonstrate that vision transformer-based methods have significantly imp…
Gradient-Guided Knowledge Distillation for Object Detectors
Qizhen Lan, Qing Tian
Deep learning models have demonstrated remarkable success in object detection, yet their complexity and computational intensity pose a barrier to deploying them in real-world appli…
TMHOI: Translational Model for Human-Object Interaction Detection
Lijing Zhu, Qizhen Lan, Alvaro Velasquez +4
Detecting human-object interactions (HOIs) is an intricate challenge in the field of computer vision. Existing methods for HOI detection heavily rely on appearance-based features,…
Adaptive Instance Distillation for Object Detection in Autonomous Driving
Qizhen Lan, Qing Tian
In recent years, knowledge distillation (KD) has been widely used to derive efficient models. Through imitating a large teacher model, a lightweight student model can achieve compa…