Research
My primary research interests lie in the area of multi-modal learning, natural language processing, and machine learning.
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CoDi: Any-to-Any Generation via Composable Diffusion
Zineng Tang,
Ziyi Yang,
Yang Liu,
Chenguang Zhu,
Michael Zeng,
Mohit Bansal
Arxiv, 2023
Project Page
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github
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arXiv
We built a framework for any to any modality mapping.
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Unifying Vision, Text, and Layout for Universal Document Processing
Zineng Tang,
Ziyi Yang,
Guoxin Wang,
Yuwei Fang,
Yang Liu,
Chenguang Zhu,
Michael Zeng,
Cha Zhang,
Mohit Bansal
CVPR, 2023 (Highlight; 2.5% acceptance rate)
github
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arXiv
We built a unified framework for document processing.
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TVLT: Textless Vision-Language Transformer
Zineng Tang*,
Jaemin Cho*,
Yixin Nie*,
Mohit Bansal
NeurIPS, 2022 (Oral; 1.76% acceptance rate)
github
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arXiv
We built a textless vision-language transformer with a minimalist design.
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Vidlankd: Improving language understanding via video-distilled knowledge transfer
Zineng Tang,
Jaemin Cho,
Hao Tan,
Mohit Bansal
NeurIPS, 2021
github
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arXiv
We built a teacher-student transformer for visually grounded language learning.
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Decembert: Learning from noisy instructional videos via dense captions and entropy minimization
Zineng Tang*,
Jie Lei*,
Mohit Bansal
NAACL, 2021
github
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paper
We built a video-language transformer that addresses the issues of noisy ASR data by dense captions and entropy minimization.
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Continuous language generative flow
Zineng Tang,
Shiyue Zhang,
Hyounghun Kim,
Mohit Bansal
ACL, 2021
github
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paper
We built a language framework basde on continuous generative flow.
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Dense-caption matching and frame-selection gating for temporal localization in VideoQA
Hyounghun Kim,
Zineng Tang,
Mohit Bansal
ACL, 2020
github
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arxiv
We built a video QA framework basde on various techniques like dense captions.
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UNC Chapel Hill, B.S. in Mathematics, 2019 - present
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NeurIPS 2022 Scholar Award
Awardee, Outstanding Undergraduate Researcher Award 2023. Computing Research Association (CRA)
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Website template mainly borrowed from Here
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