Search Results for author: JieLin Qiu

Found 17 papers, 3 papers with code

SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM

no code implementations7 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.

Question Answering Retrieval +1

Offline Reinforcement Learning with Imbalanced Datasets

no code implementations6 Jul 2023 Li Jiang, Sijie Cheng, 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.

D4RL Offline RL +4

Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics

no code implementations4 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.

Data Augmentation

LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos

no code implementations12 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.

Marketing Segmentation

Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment

no code implementations10 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.

Can Brain Signals Reveal Inner Alignment with Human Languages?

1 code implementation10 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.

EEG Relation +1

GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction

no code implementations2 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.

Data Augmentation

MHMS: Multimodal Hierarchical Multimedia Summarization

no code implementations7 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.

Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation

no code implementations25 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.

Data Augmentation

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