21 citations · 29 across the 8 of their papers we have counts for
8 papers
In-Loop Filtering Using Learned Look-Up Tables for Video Coding
Zhuoyuan Li, Jiacheng Li, Yao Li +4
In-loop filtering (ILF) is a key technology in video coding standards to reduce artifacts and enhance visual quality. Recently, neural network-based ILF schemes have achieved remar…
CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
Jinlong Li, Xinyu Liu, Baolu Li +4
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy. Although recent studies have highlighted the efficacy of vehicle-to-e…
Backdoor Attacks against Hybrid Classical-Quantum Neural Networks
Ji Guo, Wenbo Jiang, Rui Zhang +3
Hybrid Quantum Neural Networks (HQNNs) represent a promising advancement in Quantum Machine Learning (QML), yet their security has been rarely explored. In this paper, we present t…
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments
Mansur Arief, Mike Timmerman, Jiachen Li +2
Training intelligent agents to navigate highly interactive environments presents significant challenges. While guided meta reinforcement learning (RL) approach that first trains a…
In-Loop Filtering via Trained Look-Up Tables
Zhuoyuan Li, Jiacheng Li, Yao Li +3
In-loop filtering (ILF) is a key technology for removing the artifacts in image/video coding standards. Recently, neural network-based in-loop filtering methods achieve remarkable…
CMP: Cooperative Motion Prediction with Multi-Agent Communication
Zehao Wang, Yuping Wang, Zhuoyuan Wu +4
The confluence of the advancement of Autonomous Vehicles (AVs) and the maturity of Vehicle-to-Everything (V2X) communication has enabled the capability of cooperative connected and…
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
Shiqi Lei, Kanghoon Lee, Linjing Li +2
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environm…
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
Jiachen Li, Xiaojin Gong
This work aims to adapt large-scale pre-trained vision-language models, such as contrastive language-image pretraining (CLIP), to enhance the performance of object reidentification…