no code implementations • 5 Feb 2024 • Zichen Zhu, Yang Xu, Lu Chen, Jingkai Yang, Yichuan Ma, Yiming Sun, Hailin Wen, Jiaqi Liu, Jinyu Cai, Yingzi Ma, Situo Zhang, Zihan Zhao, Liangtai Sun, Kai Yu
Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context.
no code implementations • 3 Feb 2024 • Yiming Sun, Yuhe Gao, Runxue Bao, Gregory F. Cooper, Jessi Espino, Harry Hochheiser, Marian G. Michaels, John M. Aronis, Chenxi Song, Ye Ye
Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications.
1 code implementation • 12 Oct 2023 • Runxue Bao, Yiming Sun, Yuhe Gao, Jindong Wang, Qiang Yang, Haifeng Chen, Zhi-Hong Mao, Ye Ye
These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption.
no code implementations • 29 Jun 2023 • Yuelyu Ji, Yuhe Gao, Runxue Bao, Qi Li, Disheng Liu, Yiming Sun, Ye Ye
Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge.
1 code implementation • ICCV 2023 • Yiming Sun, Bing Cao, Pengfei Zhu, QinGhua Hu
The MoLE performs specialized learning of multi-modal local features, prompting the fused images to retain the local information in a sample-adaptive manner, while the MoGE focuses on the global information that complements the fused image with overall texture detail and contrast.
1 code implementation • ACMMM 2022 • Yiming Sun, Bing Cao, Pengfei Zhu, QinGhua Hu
We cascade the image fusion network with the detection networks of both modalities and use the detection loss of the fused images to provide guidance on task-related information for the optimization of the image fusion network.
no code implementations • 13 Jul 2022 • Cheng Chen, Yi Li, Yiming Sun
Active regression considers a linear regression problem where the learner receives a large number of data points but can only observe a small number of labels.
no code implementations • 16 Jul 2021 • Yifei Jiang, Yi Li, Yiming Sun, Jiaxin Wang, David P. Woodruff
A natural way to do this would be to simply apply $f$ to each entry of $A$, and then compute the matrix decomposition, but this requires storing all of $A$ as well as multiple passes over its entries.
1 code implementation • 19 May 2021 • Yiming Sun, Feng Chen, Zhiyu Chen, Mingjie Wang
However, the perturbations of global point are not effective for misleading the victim model.
no code implementations • 30 Apr 2021 • Yiming Sun, Yang Guo, Joel A. Tropp, Madeleine Udell
The TRP map is formed as the Khatri-Rao product of several smaller random projections, and is compatible with any base random projection including sparse maps, which enable dimension reduction with very low query cost and no floating point operations.
1 code implementation • 16 Mar 2020 • Pengfei Zhu, Jiayu Zheng, Dawei Du, Longyin Wen, Yiming Sun, QinGhua Hu
Moreover, an agent sharing network (ASNet) is proposed by self-supervised template sharing and view-aware fusion of the target from multiple drones, which can improve the tracking accuracy significantly compared with single drone tracking.
2 code implementations • 5 Mar 2020 • Yiming Sun, Bing Cao, Pengfei Zhu, QinGhua Hu
To address this dilemma, we further propose an uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to extract complementary information from cross-modal images, which can significantly improve the detection performance in low light conditions.
1 code implementation • 4 Jan 2020 • Yifei Li, Kuangyan Song, Yiming Sun, Liao Zhu
This paper has proposed a new baseline deep learning model of more benefits for image classification.
no code implementations • 29 Apr 2019 • Yu-jia Zhang, Kuangyan Song, Yiming Sun, Sarah Tan, Madeleine Udell
Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms.
2 code implementations • 24 Apr 2019 • Yiming Sun, Yang Guo, Charlene Luo, Joel Tropp, Madeleine Udell
This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor.
no code implementations • 3 Dec 2018 • Yiming Sun, Yige Li, Amy Kuceyeski, Sumanta Basu
Spectral density matrix estimation of multivariate time series is a classical problem in time series and signal processing.
no code implementations • 31 Jan 2018 • Congzheng Song, Yiming Sun
Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature.