1 code implementation • 24 Sep 2020 • Chenwei Wu, Chenzhuang Du, Yang Yuan
In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data.
no code implementations • NeurIPS 2021 • Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen, Hang Zhao, Longbo Huang
The world provides us with data of multiple modalities.
no code implementations • 21 Jun 2021 • Chenzhuang Du, Tingle Li, Yichen Liu, Zixin Wen, Tianyu Hua, Yue Wang, Hang Zhao
We name this problem Modality Failure, and hypothesize that the imbalance of modalities and the implicit bias of common objectives in fusion method prevent encoders of each modality from sufficient feature learning.
Ranked #60 on Semantic Segmentation on NYU Depth v2
no code implementations • 26 Jun 2021 • Yue Zhao, Chenzhuang Du, Hang Zhao, Tiejun Li
In vision-based reinforcement learning (RL) tasks, it is prevalent to assign auxiliary tasks with a surrogate self-supervised loss so as to obtain more semantic representations and improve sample efficiency.
no code implementations • 29 Sep 2021 • Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Yue Wang, Yang Yuan, Hang Zhao
We name this problem of multi-modal training, \emph{Modality Laziness}.
1 code implementation • 2 May 2023 • Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Tianyuan Yuan, Yue Wang, Yang Yuan, Hang Zhao
We abstract the features (i. e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions.
no code implementations • 6 Jun 2023 • Chenxu Hu, Jie Fu, Chenzhuang Du, Simian Luo, Junbo Zhao, Hang Zhao
Large language models (LLMs) with memory are computationally universal.
no code implementations • 8 Oct 2023 • Chenzhuang Du, Yue Zhao, Chonghua Liao, Jiacheng You, Jie Fu, Hang Zhao
To this end, we introduce Multi-Modal Low-Rank Adaptation learning (MMLoRA).
no code implementations • 10 Oct 2023 • Siting Li, Chenzhuang Du, Yue Zhao, Yu Huang, Hang Zhao
With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention.