5 citations · 6 across the 10 of their papers we have counts for
12 papers
SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Large scale pretrained models have revolutionized Natural Language Processing (NLP) and Computer Vision (CV), showcasing remarkable cross domain generalization abilities. However,…
Synergistic Prompting for Robust Visual Recognition with Missing Modalities
Zhihui Zhang, Luanyuan Dai, Qika Lin +5
Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, i…
What Really Matters for Robust Multi-Sensor HD Map Construction?
Xiaoshuai Hao, Yuting Zhao, Yuheng Ji +5
High-definition (HD) map construction methods are crucial for providing precise and comprehensive static environmental information, which is essential for autonomous driving system…
SafeMap: Robust HD Map Construction from Incomplete Observations
Xiaoshuai Hao, Lingdong Kong, Rong Yin +4
Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMa…
Multi-Modal Molecular Representation Learning via Structure Awareness
Rong Yin, Ruyue Liu, Xiaoshuai Hao +4
Accurate extraction of molecular representations is a critical step in the drug discovery process. In recent years, significant progress has been made in molecular representation l…
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning
Ruyue Liu, Rong Yin, Yong Liu +4
Graph Contrastive Learning (GCL) has emerged as the foremost approach for self-supervised learning on graph-structured data. GCL reduces reliance on labeled data by learning robust…
MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map Construction
Xiaoshuai Hao, Yunfeng Diao, Mengchuan Wei +7
Map construction task plays a vital role in providing precise and comprehensive static environmental information essential for autonomous driving systems. Primary sensors include c…
KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing
Shu Zhao, Tan Yu, Xiaoshuai Hao +2
Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency. However, existing deep hashing methods predominantly…
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized…
FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learning
Jinhui Pang, Changqing Lin, Xiaoshuai Hao +5
Continual graph learning (CGL) is an important and challenging task that aims to extend static GNNs to dynamic task flow scenarios. As one of the mainstream CGL methods, the experi…
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation
Xiaoshuai Hao, Ruikai Li, Hui Zhang +7
Online high-definition (HD) map construction is an important and challenging task in autonomous driving. Recently, there has been a growing interest in cost-effective multi-view ca…
What Foundation Models can Bring for Robot Learning in Manipulation : A Survey
Dingzhe Li, Yixiang Jin, Yuhao Sun +11
The realization of universal robots is an ultimate goal of researchers. However, a key hurdle in achieving this goal lies in the robots' ability to manipulate objects in their unst…