Search Results for author:
Found 26 papers, 9 papers with code
Towards Multi-modal Graph Large Language Model
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.
OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning
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.
Learning a Unified Degradation-aware Representation Model for Multi-modal Image Fusion
To address these limitations, we present LURE, a Learning-driven Unified Representation model for infrared and visible Image Fusion, which is degradation-aware.
One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion
Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms.
VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding
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.
Multi-Modal Generative AI: Multi-modal LLM, Diffusion and Beyond
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.
Multi-sentence Video Grounding for Long Video Generation
(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.
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
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.
CoMoFusion: Fast and High-quality Fusion of Infrared and Visible Image with Consistency Model
Generative models are widely utilized to model the distribution of fused images in the field of infrared and visible image fusion.
Causal-aware Graph Neural Architecture Search under Distribution Shifts
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.
DisenStudio: Customized Multi-subject Text-to-Video Generation with Disentangled Spatial Control
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.
RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
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.
Exploring the Potential of Large Language Models in Graph Generation
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
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.
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision
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.
BusReF: Infrared-Visible images registration and fusion focus on reconstructible area using one set of features
We focus on Infrared-Visible image registration and fusion task (IVRF).
LLM4VG: Large Language Models Evaluation for Video Grounding
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models.
Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
We first model human albumin prediction as a dynamic graph regression problem to model the dynamics and patient relationship.
Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
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.
LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
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
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
To the best of our knowledge, our work is the first benchmark for graph neural architecture search.
Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum
To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances.
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?
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.
Graph Differentiable Architecture Search with Structure Learning
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.
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
