Search Results for author: Zeyang Zhang

Found 26 papers, 9 papers with code

Towards Multi-modal Graph Large Language Model

no code implementations11 Jun 2025 Xin Wang, Zeyang Zhang, Linxin Xiao, Haibo Chen, Chendi Ge, Wenwu Zhu

To bridge this gap, we explore the potential of Multi-modal Graph Large Language Models (MG-LLM) to unify and generalize across diverse multi-modal graph data and tasks.

Graph Learning In-Context Learning +4

OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning

no code implementations24 Mar 2025 Hui Li, Congcong Bian, Zeyang Zhang, Xiaoning Song, Xi Li, Xiao-Jun Wu

Image fusion is a crucial technique in the field of computer vision, and its goal is to generate high-quality fused images and improve the performance of downstream tasks.

Contrastive Learning Infrared And Visible Image Fusion

Learning a Unified Degradation-aware Representation Model for Multi-modal Image Fusion

no code implementations10 Mar 2025 Haolong Ma, Hui Li, Chunyang Cheng, Zeyang Zhang, Xiaoning Song, Xiao-Jun Wu

To address these limitations, we present LURE, a Learning-driven Unified Representation model for infrared and visible Image Fusion, which is degradation-aware.

Infrared And Visible Image Fusion

One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion

1 code implementation CVPR 2025 Chunyang Cheng, Tianyang Xu, ZhenHua Feng, XiaoJun Wu, ZhangyongTang, Hui Li, Zeyang Zhang, Sara Atito, Muhammad Awais, Josef Kittler

Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms.

All

VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding

1 code implementation11 Oct 2024 Houlun Chen, Xin Wang, Hong Chen, Zeyang Zhang, Wei Feng, Bin Huang, Jia Jia, Wenwu Zhu

In this paper, we propose a more challenging fine-grained VCMR benchmark requiring methods to localize the best-matched moment from the corpus with other partially matched candidates.

Hallucination Moment Retrieval +4

Multi-Modal Generative AI: Multi-modal LLM, Diffusion and Beyond

no code implementations23 Sep 2024 Hong Chen, Xin Wang, Yuwei Zhou, Bin Huang, YiPeng Zhang, Wei Feng, Houlun Chen, Zeyang Zhang, Siao Tang, Wenwu Zhu

Particularly, two dominant families of techniques are: i) The multi-modal large language model (MLLM) such as GPT-4V, which shows impressive ability for multi-modal understanding; ii) The diffusion model such as Sora, which exhibits remarkable multi-modal powers, especially with respect to visual generation.

Language Modelling Large Language Model +2

Multi-sentence Video Grounding for Long Video Generation

no code implementations18 Jul 2024 Wei Feng, Xin Wang, Hong Chen, Zeyang Zhang, Wenwu Zhu

(iii) We also attempt video morphing and personalized generation methods to improve the subject consistency of long video generation, providing ablation experimental results for the subtasks of long video generation.

Moment Retrieval Retrieval +4

Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification

no code implementations24 Jun 2024 Beini Xie, Heng Chang, Ziwei Zhang, Zeyang Zhang, Simin Wu, Xin Wang, Yuan Meng, Wenwu Zhu

To search for optimal lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural Architecture Search with Graph SparsIfication and Network Pruning (GASSIP) method.

Graph Neural Network Network Pruning +2

CoMoFusion: Fast and High-quality Fusion of Infrared and Visible Image with Consistency Model

1 code implementation31 May 2024 Zhiming Meng, Hui Li, Zeyang Zhang, Zhongwei Shen, Yunlong Yu, Xiaoning Song, XiaoJun Wu

Generative models are widely utilized to model the distribution of fused images in the field of infrared and visible image fusion.

Infrared And Visible Image Fusion

Causal-aware Graph Neural Architecture Search under Distribution Shifts

no code implementations26 May 2024 Peiwen Li, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Jialong Wang, Yang Li, Wenwu Zhu

We propose to handle the distribution shifts in the graph architecture search process by discovering and exploiting the causal relationship between graphs and architectures to search for the optimal architectures that can generalize under distribution shifts.

Graph Embedding Neural Architecture Search +1

DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control

no code implementations21 May 2024 Hong Chen, Xin Wang, YiPeng Zhang, Yuwei Zhou, Zeyang Zhang, Siao Tang, Wenwu Zhu

To tackle the problems, in this paper, we propose DisenStudio, a novel framework that can generate text-guided videos for customized multiple subjects, given few images for each subject.

Attribute Motion Generation +2

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model

no code implementations23 Apr 2024 Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, Wenweu Zhu

In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis.

Causal Discovery graph construction +3

Exploring the Potential of Large Language Models in Graph Generation

no code implementations21 Mar 2024 Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Xu Chu, Yuekui Yang, Wenwu Zhu, Hong Mei

In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.

Drug Discovery Graph Generation +1

Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts

1 code implementation NeurIPS 2023 Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu

In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.

Link Prediction Node Classification

Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision

no code implementations NeurIPS 2023 Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao Shen, Shiqi Shen, Wenwu Zhu

To address the challenge, we propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures capturing various latent graph factors in a self-supervised fashion based on unlabeled graph data.

Disentanglement Neural Architecture Search

LLM4VG: Large Language Models Evaluation for Video Grounding

no code implementations21 Dec 2023 Wei Feng, Xin Wang, Hong Chen, Zeyang Zhang, Houlun Chen, Zihan Song, Yuwei Zhou, Yuekui Yang, Haiyang Wu, Wenwu Zhu

Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models.

Image Captioning Video Grounding +1

Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion

no code implementations24 Nov 2023 Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu

In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i. e., structures and features whose predictive abilities are stable across distribution shifts.

Graph Attention Graph Neural Network

LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?

1 code implementation26 Oct 2023 Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Wenwu Zhu

Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks.

Graph Meets LLMs: Towards Large Graph Models

1 code implementation28 Aug 2023 Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, Wenwu Zhu

In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models.

NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

1 code implementation18 Jun 2022 Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu

To the best of our knowledge, our work is the first benchmark for graph neural architecture search.

Benchmarking Graph Neural Network +1

Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum

1 code implementation7 Apr 2022 Zeyang Zhang, Ziwei Zhang, Xin Wang, Wenwu Zhu

To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances.

Combinatorial Optimization

Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?

no code implementations23 Dec 2021 Ziwei Zhang, Xin Wang, Zeyang Zhang, Peng Cui, Wenwu Zhu

Based on the experimental results, we advocate that TinvNN should be considered a new starting point and an essential baseline for further studies of transformation-invariant geometric deep learning.

All Combinatorial Optimization +2

Graph Differentiable Architecture Search with Structure Learning

no code implementations NeurIPS 2021 Yijian Qin, Xin Wang, Zeyang Zhang, Wenwu Zhu

Extensive experiments on real-world graph datasets demonstrate that our proposed GASSO model is able to achieve state-of-the-art performance compared with existing baselines.

Denoising Graph structure learning +1

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