no code implementations • 19 Mar 2024 • JieLin Qiu, William Han, Winfred Wang, Zhengyuan Yang, Linjie Li, JianFeng Wang, Christos Faloutsos, Lei LI, Lijuan Wang
Open-domain real-world entity recognition is essential yet challenging, involving identifying various entities in diverse environments.
no code implementations • 7 Mar 2024 • JieLin Qiu, Andrea Madotto, Zhaojiang Lin, Paul A. Crook, Yifan Ethan Xu, Xin Luna Dong, Christos Faloutsos, Lei LI, Babak Damavandi, Seungwhan Moon
We have developed the \textbf{SnapNTell Dataset}, distinct from traditional VQA datasets: (1) It encompasses a wide range of categorized entities, each represented by images and explicitly named in the answers; (2) It features QA pairs that require extensive knowledge for accurate responses.
no code implementations • 6 Jul 2023 • Li Jiang, Sijie Chen, JieLin Qiu, Haoran Xu, Wai Kin Chan, Zhao Ding
The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models.
1 code implementation • 7 Jun 2023 • JieLin Qiu, Jiacheng Zhu, William Han, Aditesh Kumar, Karthik Mittal, Claire Jin, Zhengyuan Yang, Linjie Li, JianFeng Wang, Ding Zhao, Bo Li, Lijuan Wang
To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the \textbf{MMSum} dataset.
no code implementations • 16 Apr 2023 • JieLin Qiu, Peide Huang, Makiya Nakashima, Jaehyun Lee, Jiacheng Zhu, Wilson Tang, Pohao Chen, Christopher Nguyen, Byung-Hak Kim, Debbie Kwon, Douglas Weber, Ding Zhao, David Chen
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare.
no code implementations • 13 Apr 2023 • JieLin Qiu, Jiacheng Zhu, Shiqi Liu, William Han, Jingqi Zhang, Chaojing Duan, Michael Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao
Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies.
1 code implementation • CVPR 2023 • Bo He, Jun Wang, JieLin Qiu, Trung Bui, Abhinav Shrivastava, Zhaowen Wang
The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries.
Ranked #3 on Supervised Video Summarization on SumMe
Extractive Text Summarization Supervised Video Summarization
no code implementations • 4 Feb 2023 • Jiacheng Zhu, JieLin Qiu, Aritra Guha, Zhuolin Yang, XuanLong Nguyen, Bo Li, Ding Zhao
Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.
no code implementations • 21 Jan 2023 • JieLin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Michael Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao
The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection.
no code implementations • 15 Dec 2022 • JieLin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li
Multimodal image-text models have shown remarkable performance in the past few years.
no code implementations • 21 Oct 2022 • Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, JieLin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
no code implementations • 12 Oct 2022 • JieLin Qiu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Ding Zhao, Hailin Jin
Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials.
no code implementations • 10 Oct 2022 • JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.
1 code implementation • 10 Aug 2022 • William Han, JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao
In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions.
no code implementations • 2 Aug 2022 • Jiacheng Zhu, JieLin Qiu, Zhuolin Yang, Douglas Weber, Michael A. Rosenberg, Emerson Liu, Bo Li, Ding Zhao
In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals.
no code implementations • 7 Apr 2022 • JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.
no code implementations • 25 Jan 2022 • JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Peide Huang, Michael Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection.