4 papers
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…
Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing
Luu Tung Hai, Thinh D. Le, Zhicheng Ding +2
Point cloud processing has gained significant attention due to its critical role in applications such as autonomous driving and 3D object recognition. However, deploying high-perfo…
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…