most citedMHMS: Multimodal Hierarchical Multimedia Summarization

12 citations · 17 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20221 cited

Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables

Mengdi Xu, Peide Huang, Yaru Niu +8

One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result i…

cs.CV2022

LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos

Jielin Qiu, Franck Dernoncourt, Trung Bui +3

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 s…

cs.CV20224 cited

Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment

Jielin Qiu, Jiacheng Zhu, Mengdi Xu +6

Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding. It plays an essential role in real-world applications, i.e., automa…

cs.CV202212 cited

MHMS: Multimodal Hierarchical Multimedia Summarization

Jielin Qiu, Jiacheng Zhu, Mengdi Xu +6

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 p…

cs.LG2019

Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis

Wei Liu, Jie-Lin Qiu, Wei-Long Zheng +1

Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical c…