1 code implementation • 22 Apr 2024 • Yuying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song, Chen Li, Xiaohan Ding, Ying Shan
We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
no code implementations • 20 Apr 2024 • Guangyin Bao, Zixuan Gong, Qi Zhang, Jialei Zhou, Wei Fan, Kun Yi, Usman Naseem, Liang Hu, Duoqian Miao
We meticulously evaluate the performance of our approach across coarse-grained and fine-grained visual decoding tasks.
no code implementations • 23 Feb 2024 • Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
1 code implementation • 18 Dec 2023 • An Lao, Qi Zhang, Chongyang Shi, Longbing Cao, Kun Yi, Liang Hu, Duoqian Miao
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media.
1 code implementation • 27 Nov 2023 • Weixian Lei, Yixiao Ge, Kun Yi, Jianfeng Zhang, Difei Gao, Dylan Sun, Yuying Ge, Ying Shan, Mike Zheng Shou
In this paper, we present ViT-Lens-2 that facilitates efficient omni-modal representation learning by perceiving novel modalities with a pretrained ViT and aligning them to a pre-defined space.
2 code implementations • NeurIPS 2023 • Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Defu Lian, Ning An, Longbing Cao, Zhendong Niu
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
1 code implementation • 20 Aug 2023 • Weixian Lei, Yixiao Ge, Jianfeng Zhang, Dylan Sun, Kun Yi, Ying Shan, Mike Zheng Shou
A well-trained lens with a ViT backbone has the potential to serve as one of these foundation models, supervising the learning of subsequent modalities.
Ranked #2 on Zero-Shot Transfer 3D Point Cloud Classification on ModelNet40 (using extra training data)
no code implementations • 4 Feb 2023 • Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, Hui Xiong
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
1 code implementation • 27 Jan 2023 • Hui He, Qi Zhang, Shoujin Wang, Kun Yi, Zhendong Niu, Longbing Cao
To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables.
1 code implementation • CVPR 2023 • Shusheng Yang, Yixiao Ge, Kun Yi, Dian Li, Ying Shan, XiaoHu Qie, Xinggang Wang
Both masked image modeling (MIM) and natural language supervision have facilitated the progress of transferable visual pre-training.
no code implementations • 6 Oct 2022 • Kun Yi, Qi Zhang, Liang Hu, Hui He, Ning An, Longbing Cao, Zhendong Niu
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
no code implementations • 1 Sep 2022 • Hui He, Qi Zhang, Kun Yi, Kaize Shi, Zhendong Niu, Longbing Cao
Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time.
1 code implementation • 19 May 2022 • Kun Yi, Yixiao Ge, Xiaotong Li, Shusheng Yang, Dian Li, Jianping Wu, Ying Shan, XiaoHu Qie
Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up.
1 code implementation • 29 Mar 2022 • Xiaotong Li, Yixiao Ge, Kun Yi, Zixuan Hu, Ying Shan, Ling-Yu Duan
Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning.
no code implementations • 17 Feb 2022 • Kun Yi, Guo-Hua Wang, Jianxin Wu
It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically.
1 code implementation • 13 Oct 2021 • Kun Yi, Ryu Yamagishi, Taishan Li, Zhengyang Bai, Qiang Ma
Our mechanism include two components: one is a probabilistic model that reveals the user behaviors in tourism; the other is a pseudo rating mechanism to handle the cold-start issue in POIs recommendations.
3 code implementations • CVPR 2019 • 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.
Ranked #25 on Image Classification on Clothing1M (using extra training data)