6 citations · 6 across the 10 of their papers we have counts for
10 papers
AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization
Qiyang Li, Rui Kong, Yuchen Li +5
The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Mode…
FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding
Yuchen Li, Rui Kong, Zhonghao Lyu +11
Deploying large language models (LLMs) in mobile and edge computing environments is constrained by limited on-device resources, scarce wireless bandwidth, and frequent model evolut…
QuantFace: Efficient Quantization for Face Restoration
Jiatong Li, Libo Zhu, Haotong Qin +5
Diffusion models have been achieving remarkable performance in face restoration. However, the heavy computations hamper the widespread adoption of these models. In this work, we pr…
Low-bit Model Quantization for Deep Neural Networks: A Survey
Kai Liu, Qian Zheng, Kaiwen Tao +9
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…
B2LoRa: Boosting LoRa Transmission for Satellite-IoT Systems with Blind Coherent Combining
Yimin Zhao, Weibo Wang, Xiong Wang +6
With the rapid growth of Low Earth Orbit (LEO) satellite networks, satellite-IoT systems using the LoRa technique have been increasingly deployed to provide widespread Internet ser…
DVD-Quant: Data-free Video Diffusion Transformers Quantization
Zhiteng Li, Hanxuan Li, Junyi Wu +6
Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While…
Improving SAM for Camouflaged Object Detection via Dual Stream Adapters
Jiaming Liu, Linghe Kong, Guihai Chen
Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natural images, but its performance on camouflaged object detection (COD) is unsatisfa…
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)
Yuchen Li, Haoyi Xiong, Linghe Kong +4
Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from retrieved content based on input queries. However, traditional LTR models encounter…
2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
Kai Liu, Haotong Qin, Yong Guo +4
Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit paramete…
Image Super-Resolution with Text Prompt Diffusion
Zheng Chen, Yulun Zhang, Jinjin Gu +4
Image super-resolution (SR) methods typically model degradation to improve reconstruction accuracy in complex and unknown degradation scenarios. However, extracting degradation inf…