36 citations · 53 across the 8 of their papers we have counts for
8 papers
In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics
Xiaomeng Fu, Junfan Lin, Yang Liu +4
Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and…
High-fidelity and Lip-synced Talking Face Synthesis via Landmark-based Diffusion Model
Weizhi Zhong, Junfan Lin, Peixin Chen +2
Audio-driven talking face video generation has attracted increasing attention due to its huge industrial potential. Some previous methods focus on learning a direct mapping from au…
DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback
Junfan Lin, Yuying Zhu, Lingbo Liu +3
Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network, which in turn enhances fuel utilization efficiency, air quality, and road safety,…
Visual Tuning
Bruce X. B. Yu, Jianlong Chang, Haixin Wang +9
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visu…
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training
Junfan Lin, Jianlong Chang, Lingbo Liu +4
Text-to-motion generation is an emerging and challenging problem, which aims to synthesize motion with the same semantics as the input text. However, due to the lack of diverse lab…
Road Network Guided Fine-Grained Urban Traffic Flow Inference
Lingbo Liu, Mengmeng Liu, Guanbin Li +3
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of the required traffic monitor…
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp
Junfan Lin, Zhongzhan Huang, Keze Wang +3
Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it's still challenging to apply it to real-world tasks, due to the po…
Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis
Junfan Lin, Keze Wang, Ziliang Chen +2
Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases. However, since the dialogue reco…