3 citations · 4 across the 14 of their papers we have counts for
18 papers
STCFormer: Adaptive Spatio-Temporal Modeling with Dynamic Cluster Transformer for Station-based Weather Forecasting
Rongwen Li, Haixin Xie, Mingyang Wang +5
Station-based weather forecasting supports daily life and economic activity, yet accurate forecasts require modeling complex spatial dependencies among stations. Recent clustering-…
MWeather: A Benchmark for Joint Multi-Station and Multi-Variable Weather Forecasting
Rongwen Li, Xiao Wang, Mingyang Wang +4
Station weather forecasting is fundamentally shaped by both complex spatial dependencies across stations and strong physical coupling among weather variables. However, existing stu…
ProjFormer: Point Cloud Completion via Geometric-Projective Transformer and Cross-Modal Semantic Constraints
Sheng Liu, Meng Wang, Ruihui Li +3
Point cloud completion is inherently ill-posed due to severe sparsity and ambiguity in partial observations. Existing multi-view methods alleviate this by incorporating 2D semantic…
Human-Guided Causal Knowledge Injection for Virtual Cells
Pengcheng Wang, Changjian Chen, Zhuo Tang +4
Virtual cells employ machine learning models to simulate and predict cellular behaviors, serving as a critical computational framework for investigating health and disease. Injecti…
Geospatial-Prior Guidance for 3D Semantic Scene Completion
Meng Wang, Shougao Zhang, Wenzhe He +4
Inferring complete 3D geometry and semantics from onboard images remains challenging because occlusions and restricted fields of view leave large scene regions underconstrained. Al…
MCI-SQL: Text-to-SQL with Metadata-Complete Context and Intermediate Correction
Qin Wang, Youhuan Li, Suixi Lin +5
Text-to-SQL aims to translate natural language queries into SQL statements. Existing methods typically follow a pipeline of pre-processing, schema linking, candidate SQL generation…
Enhancing Small-Scale Dataset Expansion with Triplet-Connection-based Sample Re-Weighting
Ting Xiang, Changjian Chen, Zhuo Tang +5
The performance of computer vision models in certain real-world applications, such as medical diagnosis, is often limited by the scarcity of available images. Expanding datasets us…
Interactive Hybrid Rice Breeding with Parametric Dual Projection
Changjian Chen, Pengcheng Wang, Fei Lyu +6
Hybrid rice breeding crossbreeds different rice lines and cultivates the resulting hybrids in fields to select those with desirable agronomic traits, such as higher yields. Recentl…
Leveraging Pre-trained Large Language Models with Refined Prompting for Online Task and Motion Planning
Huihui Guo, Huilong Pi, Yunchuan Qin +2
With the rapid advancement of artificial intelligence, there is an increasing demand for intelligent robots capable of assisting humans in daily tasks and performing complex operat…
VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion
Meng Wang, Huilong Pi, Ruihui Li +3
Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving. However, images provide limited information making the mode…
Vision-based 3D Semantic Scene Completion via Capture Dynamic Representations
Meng Wang, Fan Wu, Yunchuan Qin +3
The vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, the presence of dynamic objects in th…
AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs
Zihao Zeng, Chubo Liu, Xin He +5
Transformer-based large language models (LLMs) have demonstrated exceptional capabilities in sequence modeling and text generation, with improvements scaling proportionally with mo…
Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance
Meng Wang, Fan Wu, Ruihui Li +3
3D Semantic Scene Completion (SSC) provides comprehensive scene geometry and semantics for autonomous driving perception, which is crucial for enabling accurate and reliable decisi…
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models
Yingen Liu, Fan Wu, Ruihui Li +2
Multimodal large language models (MLLMs) demonstrate strong performance across visual tasks, but their efficiency is hindered by significant computational and memory demands from p…
Enhancing the Prediction of Glass Dynamics by Incorporating the Direction of Deviation from Equilibrium Positions
Xiao Jiang, Zean Tian, Kenli Li +1
Elucidating the intricate relationship between the structure and dynamics in the context of the glass transition has been a persistent challenge. Machine learning (ML) has emerged…
Towards interpreting the thermally activated dynamics in metallic glass with the structural constraint neural network
Xiao Jiang, Zean Tian, Kenli Li +1
Unraveling the structural factors influencing the dynamics of amorphous solids is crucial. While deep learning aids in navigating these complexities, transparency issues persist. I…
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
Zhiwei Zuo, Zhuo Tang, Bin Wang +2
New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledg…
Machine unlearning through fine-grained model parameters perturbation
Zhiwei Zuo, Zhuo Tang, Kenli Li +1
Machine unlearning techniques, which involve retracting data records and reducing influence of said data on trained models, help with the user privacy protection objective but incu…