3 citations · 7 across the 17 of their papers we have counts for
18 papers
Resilience Inference for Supply Chains with Hypergraph Neural Network
Zetian Shen, Hongjun Wang, Jiyuan Chen +1
Supply chains are integral to global economic stability, yet disruptions can swiftly propagate through interconnected networks, resulting in substantial economic impacts. Accurate…
HarmoQ: Harmonized Post-Training Quantization for High-Fidelity Image
Hongjun Wang, Jiyuan Chen, Xuan Song +1
Post-training quantization offers an efficient pathway to deploy super-resolution models, yet existing methods treat weight and activation quantization independently, missing their…
Not All Degradations Are Equal: A Targeted Feature Denoising Framework for Generalizable Image Super-Resolution
Hongjun Wang, Jiyuan Chen, Zhengwei Yin +2
Generalizable Image Super-Resolution aims to enhance model generalization capabilities under unknown degradations. To achieve this goal, the models are expected to focus only on im…
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Xin Jin +134
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are devel…
Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting
Hongjun Wang, Jiyuan Chen, Lingyu Zhang +2
Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. How…
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture comple…
Learning to Balance: Diverse Normalization for Cloth-Changing Person Re-Identification
Hongjun Wang, Jiyuan Chen, Zhengwei Yin +2
Cloth-Changing Person Re-Identification (CC-ReID) involves recognizing individuals in images regardless of clothing status. In this paper, we empirically and experimentally demonst…
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, urban environments are constantly ev…
Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting
Hongjun Wang, Jiyuan Chen, Yinqiang Zheng +1
Climate change-driven floods demand advanced forecasting models, yet Graph Neural Networks (GNNs) underutilize river network topology due to tree-like structures causing over-squas…
Robust Traffic Forecasting against Spatial Shift over Years
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both t…
Worldwide wildfire spreading and its severity described by the SIR model
Tong Pan, Hongjun Wang, Jiyuan Chen +1
Global wildfire spreading dynamics and severity are analyzed using the susceptible-infected-recovered (SIR) compartment model. We use the novel FireTracks (FT) Scientific Dataset c…
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution
Hongjun Wang, Jiyuan Chen, Yinqiang Zheng +1
Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. %Despite the substantial advancement% While most existing work assume…
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention
Hongjun Wang, Jiyuan Chen, Lun Du +3
Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks…
Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation
Hongjun Wang, Zhiwen Zhang, Zipei Fan +4
Travel time estimation from GPS trips is of great importance to order duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is not always available due to the…
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Hongjun Wang, Jiyuan Chen, Tong Pan +7
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, the…
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
Zhiwen Zhang, Hongjun Wang, Jiyuan Chen +3
Estimating the travel time of a path is an essential topic for intelligent transportation systems. It serves as the foundation for real-world applications, such as traffic monitori…
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
Zhiwen Zhang, Hongjun Wang, Zipei Fan +3
Due to the rapid development of Internet of Things (IoT) technologies, many online web apps (e.g., Google Map and Uber) estimate the travel time of trajectory data collected by mob…
ST-ExpertNet: A Deep Expert Framework for Traffic Prediction
Hongjun Wang, Jiyuan Chen, Zipei Fan +3
Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances. As we all know, the flow at a citywide lev…