1 citations · 3 across the 8 of their papers we have counts for
11 papers
When Vision Meets Graphs: A Survey on Graph Reasoning and Learning
Xinjian Zhao, Wei Pang, Zhixuan Yu +8
Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the past decade, Graph Neural Networks (GNNs) have dominated graph machine…
MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models
Xinjian Zhao, Xiangru Jian, Yaoyao Xu +4
Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property predictio…
FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
Xiangru Jian, Hao Xu, Wei Pang +13
The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution, yet current evaluations fai…
The Underappreciated Power of Vision Models for Graph Structural Understanding
Xinjian Zhao, Wei Pang, Zhongkai Xue +6
Graph Neural Networks operate through bottom-up message-passing, fundamentally differing from human visual perception, which intuitively captures global structures first. We invest…
LazyVLM: Neuro-Symbolic Approach to Video Analytics
Xiangru Jian, Wei Pang, Zhengyuan Dong +2
Current video analytics approaches face a fundamental trade-off between flexibility and efficiency. End-to-end Vision Language Models (VLMs) often struggle with long-context proces…
Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers
Wei Pang, Kevin Qinghong Lin, Xiangru Jian +2
Academic poster generation is a crucial yet challenging task in scientific communication, requiring the compression of long-context interleaved documents into a single, visually co…
Enhancing Graph Self-Supervised Learning with Graph Interplay
Xinjian Zhao, Wei Pang, Xiangru Jian +3
Graph self-supervised learning (GSSL) has emerged as a compelling framework for extracting informative representations from graph-structured data without extensive reliance on labe…
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning
Xiangru Jian, Xinjian Zhao, Wei Pang +4
The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues. Spectral augmentation, which involves modify…
DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models
Yimu Wang, Shuai Yuan, Bo Xue +4
Recent progress in video-text retrieval has been driven largely by advancements in model architectures and training strategies. However, the representation learning capabilities of…
InvGC: Robust Cross-Modal Retrieval by Inverse Graph Convolution
Xiangru Jian, Yimu Wang
Over recent decades, significant advancements in cross-modal retrieval are mainly driven by breakthroughs in visual and linguistic modeling. However, a recent study shows that mult…
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks
Yimu Wang, Xiangru Jian, Bo Xue
In this work, we present a post-processing solution to address the hubness problem in cross-modal retrieval, a phenomenon where a small number of gallery data points are frequently…