43 citations · 72 across the 10 of their papers we have counts for
10 papers
Role-aware Heuristic Episodic Attention for Conversational LLMs
Wanyang Hong, Zhaoning Zhang, Yi Chen +5
Large language models often lose track of persistent instructions and relevant information as multi-turn conversations grow. We study this cumulative contextual decay through three…
Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference
Libo Zhang, Zhaoning Zhang, Baizhou Xu +4
With the continuous advancement in the performance of large language models (LLMs), their demand for computational resources and memory has significantly increased, which poses maj…
Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment
Baoyun Peng, Min Liu, Zhaoning Zhang +2
Face recognition has made significant progress in recent years due to deep convolutional neural networks (CNN). In many face recognition (FR) scenarios, face images are acquired fr…
Correlation Congruence for Knowledge Distillation
Baoyun Peng, Xiao Jin, Jiaheng Liu +5
Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level. However, they usually ignore the correlation between…
ThunderNet: Towards Real-time Generic Object Detection
Zheng Qin, Zeming Li, Zhaoning Zhang +4
Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational c…
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
Hao Yu, Zhaoning Zhang, Zheng Qin +4
Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those pr…
Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions
Zheng Qin, Zhaoning Zhang, Dongsheng Li +2
Depthwise convolutions provide significant performance benefits owing to the reduction in both parameters and mult-adds. However, training depthwise convolution layers with GPUs is…
Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
Zheng Qin, Zhaoning Zhang, Shiqing Zhang +2
Compact neural networks are inclined to exploit "sparsely-connected" convolutions such as depthwise convolution and group convolution for employment in mobile applications. Compare…
FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy
Zheng Qin, Zhaoning Zhang, Xiaotao Chen +1
We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying an a…
S-OHEM: Stratified Online Hard Example Mining for Object Detection
Minne Li, Zhaoning Zhang, Hao Yu +2
One of the major challenges in object detection is to propose detectors with highly accurate localization of objects. The online sampling of high-loss region proposals (hard exampl…