24 citations · 65 across the 13 of their papers we have counts for
8 papers · 1 filter
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…