1 code implementation • 7 May 2024 • Lu Ma, Zeang Sheng, Xunkai Li, Xinyi Gao, Zhezheng Hao, Ling Yang, Wentao Zhang, Bin Cui
To address the critical challenges, a range of algorithms have been proposed to accelerate training and inference of GNNs, attracting increasing attention from the research community.
no code implementations • 21 Apr 2024 • Zhilin Huang, Yijie Yu, Ling Yang, Chujun Qin, Bing Zheng, Xiawu Zheng, Zikun Zhou, YaoWei Wang, Wenming Yang
With the advancement of AIGC, video frame interpolation (VFI) has become a crucial component in existing video generation frameworks, attracting widespread research interest.
1 code implementation • 11 Mar 2024 • Haowei Zhu, Ling Yang, Jun-Hai Yong, Wentao Zhang, Bin Wang
In this paper, we propose DistDiff, an effective data expansion framework based on the distribution-aware diffusion model.
2 code implementations • 29 Feb 2024 • Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, Jie Jiang, Bin Cui
We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators.
no code implementations • 27 Feb 2024 • Ling Yang, Haotian Qian, Zhilong Zhang, Jingwei Liu, Bin Cui
In this pioneering approach, we compel the model to learn manifold structures between samples in each training batch.
1 code implementation • 26 Feb 2024 • Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui
To address this issue, we propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample into forward and reverse processes.
2 code implementations • 20 Feb 2024 • Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, Bin Cui
In this paper, we propose a new training-free and transferred-friendly text-to-image generation framework, namely RealCompo, which aims to leverage the advantages of text-to-image and layout-to-image models to enhance both realism and compositionality of the generated images.
1 code implementation • 22 Jan 2024 • Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui
In this paper, we propose a brand new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG), harnessing the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models.
1 code implementation • 15 Jan 2024 • Zhilin Huang, Ling Yang, Zaixi Zhang, Xiangxin Zhou, Yu Bao, Xiawu Zheng, Yuwei Yang, Yu Wang, Wenming Yang
Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information.
no code implementations • NeurIPS 2023 • Ling Yang, Jingwei Liu, Shenda Hong, Zhilong Zhang, Zhilin Huang, Zheming Cai, Wentao Zhang, Bin Cui
In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context.
Ranked #1 on Image Inpainting on CelebA (LPIPS metric)
no code implementations • 4 Dec 2023 • Ling Yang, Zhanyu Wang, Zhenghao Chen, Xinyu Liang, Luping Zhou
Multimodal Large Language Models (MLLMs) have shown success in various general image processing tasks, yet their application in medical imaging is nascent, lacking tailored models.
1 code implementation • 4 Aug 2023 • Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, Jure Leskovec
To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.
1 code implementation • 28 Jun 2023 • Ling Yang, Jiayi Zheng, Heyuan Wang, Zhongyi Liu, Zhilin Huang, Shenda Hong, Wentao Zhang, Bin Cui
To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings.
1 code implementation • 21 Nov 2022 • Ling Yang, Zhilin Huang, Yang song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, Ming-Hsuan Yang
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images.
no code implementations • 6 Nov 2022 • Yafei Shen, Ling Yang
The core idea of our scheme is that in order to better detect high leverage points, we should suppress the complete reconstruction of the dataset to convert high leverage points into influential points, and it is also necessary to ensure that the differences between the eigenvalues of the covariance matrix of the original dataset and their corresponding reconstructed results in the direction of each principal component are equal.
no code implementations • 28 Oct 2022 • Shiyi Xia, Mingyang Zhao, Qian Ma, Xunnan Zhang, Ling Yang, Yazhi Pi, Hyunchul Chung, Ad Reniers, A. M. J. Koonen, Zizheng Cao
Finally, the 16/8/4 -array beam steering was demonstrated by using 4/3/2 active controllers, respectively.
2 code implementations • 2 Sep 2022 • Ling Yang, Zhilong Zhang, Yang song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, Ming-Hsuan Yang
This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.
no code implementations • 31 May 2022 • Ling Yang, Shenda Hong
Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks.
1 code implementation • 20 May 2022 • Wenrui Zhang, Ling Yang, Shijia Geng, Shenda Hong
In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a unified way.
no code implementations • 19 May 2022 • Jiayi Zheng, Ling Yang, Heyuan Wang, Cheng Yang, Yinghong Li, Xiaowei Hu, Shenda Hong
To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm.
no code implementations • 8 Feb 2022 • Ling Yang, Shenda Hong
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations.
no code implementations • 8 Feb 2022 • Ling Yang, Shenda Hong, Luxia Zhang
To the best of our knowledge, SPGN is the first to utilize spectral comparisons in different intervals and involve spectral propagation across all time series with graph networks for few-shot TSC.
no code implementations • 29 Sep 2021 • Ling Yang, Shenda Hong, Luxia Zhang
First, we revisit the augmentation methods for time series of existing works and note that they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context.
1 code implementation • CVPR 2020 • Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, Yu Liu
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #2 on Few-Shot Learning on Mini-ImageNet - 1-Shot Learning
no code implementations • 16 Apr 2019 • Yeqi Liu, Chuanyang Gong, Ling Yang, Yingyi Chen
The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series.