no code implementations • 23 Apr 2024 • Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability.
no code implementations • 25 Mar 2024 • Zhikai Chen, Fuchen Long, Zhaofan Qiu, Ting Yao, Wengang Zhou, Jiebo Luo, Tao Mei
Technically, SATeCo freezes all the parameters of the pre-trained UNet and VAE, and only optimizes two deliberately-designed spatial feature adaptation (SFA) and temporal feature alignment (TFA) modules, in the decoder of UNet and VAE.
1 code implementation • 14 Feb 2024 • Juanhui Li, Haoyu Han, Zhikai Chen, Harry Shomer, Wei Jin, Amin Javari, Jiliang Tang
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
no code implementations • 3 Feb 2024 • Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang
In this work, we delve into neural scaling laws on graphs from both model and data perspectives.
no code implementations • 3 Feb 2024 • Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang
Graph Foundation Model (GFM) is a new trending research topic in the graph domain, aiming to develop a graph model capable of generalizing across different graphs and tasks.
1 code implementation • 7 Oct 2023 • Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang
In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.
2 code implementations • 7 Jul 2023 • Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang
The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.
1 code implementation • NeurIPS 2023 • Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
no code implementations • 11 May 2023 • Junpei Liao, Zhikai Chen, Liang Yi, Wenyuan Yang, Baoyuan Wu, Xiaochun Cao
We apply adversarial attacks to VIF models and find that the VIF models are very vulnerable to adversarial examples.
1 code implementation • CVPR 2023 • Zhikai Chen, Fuchen Long, Zhaofan Qiu, Ting Yao, Wengang Zhou, Jiebo Luo, Tao Mei
Point cloud completion aims to recover the completed 3D shape of an object from its partial observation.
no code implementations • 29 Sep 2021 • Xianfeng Gao, Zhikai Chen, Bo Zhang
The experiments on ImageNet show that our method successfully mitigates the gap of transferability between models with different input sizes and achieves about 8% higher success rate comparing with the state-of-the-art input transformation methods.
no code implementations • CVPR 2021 • Zhikai Chen, Lingxi Xie, Shanmin Pang, Yong He, Bo Zhang
This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks.
no code implementations • 10 Dec 2019 • Zhikai Chen, Lingxi Xie, Shanmin Pang, Yong He, Qi Tian
There have been many efforts in attacking image classification models with adversarial perturbations, but the same topic on video classification has not yet been thoroughly studied.
no code implementations • 21 Nov 2018 • Xin Jin, Zhibo Chen, Jianxin Lin, Zhikai Chen, Wei Zhou
Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality, scalability and practicality in real-world multimedia applications.