67 citations · 113 across the 10 of their papers we have counts for
10 papers
ViT-Lens: Towards Omni-modal Representations
Weixian Lei, Yixiao Ge, Kun Yi +6
Aiming to advance AI agents, large foundation models significantly improve reasoning and instruction execution, yet the current focus on vision and language neglects the potential…
RILS: Masked Visual Reconstruction in Language Semantic Space
Shusheng Yang, Yixiao Ge, Kun Yi +4
Both masked image modeling (MIM) and natural language supervision have facilitated the progress of transferable visual pre-training. In this work, we seek the synergy between two p…
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Kun Yi, Qi Zhang, Wei Fan +7
Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time serie…
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
Hui He, Qi Zhang, Shoujin Wang +3
Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models since such models may attend/bias to certain (advantaged) variables. Addre…
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
Kun Yi, Qi Zhang, Liang Hu +4
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable rec…
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting
Hui He, Qi Zhang, Kun Yi +3
Due to the non-stationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasti…
Masked Image Modeling with Denoising Contrast
Kun Yi, Yixiao Ge, Xiaotong Li +5
Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, tha…
mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
Xiaotong Li, Yixiao Ge, Kun Yi +3
Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a class…
PENCIL: Deep Learning with Noisy Labels
Kun Yi, Guo-Hua Wang, Jianxin Wu
Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with n…
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Kun Yi, Jianxin Wu
Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with n…