24 citations · 65 across the 13 of their papers we have counts for
13 papers
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
Guangyi Chen, Yifan Shen, Zhenhao Chen +5
Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning. While some recent method…
Temporally Disentangled Representation Learning under Unknown Nonstationarity
Xiangchen Song, Weiran Yao, Yewen Fan +5
In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-re…
Subspace Identification for Multi-Source Domain Adaptation
Zijian Li, Ruichu Cai, Guangyi Chen +3
Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Although current methods achieve target…
Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer
Guangyi Chen, Xiao Liu, Guangrun Wang +4
Video-language pre-trained models have shown remarkable success in guiding video question-answering (VideoQA) tasks. However, due to the length of video sequences, training large-s…
Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment
Sheng Zhang, Muzammal Naseer, Guangyi Chen +4
Large-scale pre-trained Vision Language Models (VLMs) have proven effective for zero-shot classification. Despite the success, most traditional VLMs-based methods are restricted by…
Understanding Masked Autoencoders via Hierarchical Latent Variable Models
Lingjing Kong, Martin Q. Ma, Guangyi Chen +4
Masked autoencoder (MAE), a simple and effective self-supervised learning framework based on the reconstruction of masked image regions, has recently achieved prominent success in…
Partial Identifiability for Domain Adaptation
Lingjing Kong, Shaoan Xie, Weiran Yao +5
Unsupervised domain adaptation is critical to many real-world applications where label information is unavailable in the target domain. In general, without further assumptions, the…
Feature Expansion for Graph Neural Networks
Jiaqi Sun, Lin Zhang, Guangyi Chen +3
Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification. Recently, many methods have studi…
Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction
Guangyi Chen, Zhenhao Chen, Shunxing Fan +1
The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of fu…
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model
Shaoan Xie, Zhifei Zhang, Zhe Lin +2
Generic image inpainting aims to complete a corrupted image by borrowing surrounding information, which barely generates novel content. By contrast, multi-modal inpainting provides…
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
Yue Yu, Xuan Kan, Hejie Cui +9
Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopt…
Temporally Disentangled Representation Learning
Weiran Yao, Guangyi Chen, Kun Zhang
Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by expl…
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
Guangyi Chen, Weiran Yao, Xiangchen Song +3
With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike convention…