1 code implementation • 2 Feb 2025 • Jiaxing Xu, Yongqiang Chen, Xia Dong, Mengcheng Lan, Tiancheng Huang, Qingtian Bian, James Cheng, Yiping Ke
Graph Neural Networks (GNNs) have shown promising in analyzing brain networks, but there are two major challenges in using GNNs: (1) distribution shifts in multi-site brain network data, leading to poor Out-of-Distribution (OOD) generalization, and (2) limited interpretability in identifying key brain regions critical to neurological disorders.
1 code implementation • 25 Jan 2025 • Qingtian Bian, Marcus Vinícius de Carvalho, Tieying Li, Jiaxing Xu, Hui Fang, Yiping Ke
Another challenge lies in aligning the domain-specific and cross-domain sequences.
1 code implementation • 16 Nov 2024 • Yue Zhou, Mengcheng Lan, Xiang Li, Yiping Ke, Xue Jiang, Litong Feng, Wayne Zhang
Remote sensing (RS) visual grounding aims to use natural language expression to locate specific objects (in the form of the bounding box or segmentation mask) in RS images, enhancing human interaction with intelligent RS interpretation systems.
1 code implementation • 13 Oct 2024 • Mengcheng Lan, Chaofeng Chen, Yue Zhou, Jiaxing Xu, Yiping Ke, Xinjiang Wang, Litong Feng, Wayne Zhang
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge.
no code implementations • 28 Sep 2024 • Jiaxing Xu, Mengcheng Lan, Xia Dong, Kai He, Wei zhang, Qingtian Bian, Yiping Ke
Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange.
1 code implementation • 17 Sep 2024 • Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian, Wei Li, Tieying Li, Yiping Ke, Miao Qiao
It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism.
1 code implementation • 9 Aug 2024 • Mengcheng Lan, Chaofeng Chen, Yiping Ke, Xinjiang Wang, Litong Feng, Wayne Zhang
ProxyCLIP leverages the spatial feature correspondence from VFMs as a form of proxy attention to augment CLIP, thereby inheriting the VFMs' robust local consistency and maintaining CLIP's exceptional zero-shot transfer capacity.
Ranked #2 on
Unsupervised Semantic Segmentation with Language-image Pre-training
on PASCAL Context-60
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+2
no code implementations • 17 Jul 2024 • Mengcheng Lan, Chaofeng Chen, Yiping Ke, Xinjiang Wang, Litong Feng, Wayne Zhang
Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains challenging, producing noisy segmentation maps with mis-segmented regions.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
1 code implementation • 13 Jun 2024 • Yue Zhou, Litong Feng, Yiping Ke, Xue Jiang, Junchi Yan, Xue Yang, Wayne Zhang
Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding.
1 code implementation • NeurIPS 2023 • Mengcheng Lan, Xinjiang Wang, Yiping Ke, Jiaxing Xu, Litong Feng, Wayne Zhang
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
1 code implementation • 9 Sep 2023 • Qingtian Bian, Jiaxing Xu, Hui Fang, Yiping Ke
To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization.
2 code implementations • 7 Jul 2023 • Jiaxing Xu, Qingtian Bian, Xinhang Li, Aihu Zhang, Yiping Ke, Miao Qiao, Wei zhang, Wei Khang Jeremy Sim, Balázs Gulyás
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
1 code implementation • 25 May 2023 • Jiaxing Xu, Aihu Zhang, Qingtian Bian, Vijay Prakash Dwivedi, Yiping Ke
We first investigate different kinds of connectivities existing in a local neighborhood and identify a substructure called union subgraph, which is able to capture the complete picture of the 1-hop neighborhood of an edge.
1 code implementation • NeurIPS 2023 • Jiaxing Xu, Yunhan Yang, David Tse Jung Huang, Sophi Shilpa Gururajapathy, Yiping Ke, Miao Qiao, Alan Wang, Haribalan Kumar, Josh McGeown, Eryn Kwon
This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics.
no code implementations • 2 Sep 2022 • Jiaxing Xu, Jinjie Ni, Yiping Ke
In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification.
1 code implementation • 7 Jul 2021 • Tianbo Li, Tianze Luo, Yiping Ke, Sinno Jialin Pan
Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.
no code implementations • 6 May 2020 • Pengfei Wei, Yiping Ke, Xinghua Qu, Tze-Yun Leong
Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains.
no code implementations • NeurIPS 2019 • Tianbo Li, Yiping Ke
Experimental results on synthetic and real-world datasets validate the effectiveness of thinning in the tasks of parameter and gradient estimation, as well as stochastic optimization.
no code implementations • ICML 2017 • Pengfei Wei, Ramon Sagarna, Yiping Ke, Yew-Soon Ong, Chi-Keong Goh
A key challenge in multi-source transfer learning is to capture the diverse inter-domain similarities.
no code implementations • 26 May 2016 • Zhiqiang Xu, Yiping Ke
We generalize it to Riemannian manifolds and realize it to solve the non-convex eigen-decomposition problem.