--- title: "Wendi Zheng — author profile and papers" description: "Wendi Zheng, Tianjin University of Science and Technology: 3 papers on Elamur Research (2021–2024), h-index 18 — affiliations, topics, co-authors and who…" canonical: "https://research.elamur.ai/free/en/authors/wendi-zheng/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/wendi-zheng/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Wendi Zheng # Wendi Zheng Wendi Zheng works on large generative models built on the transformer architecture, spanning language, images and video. Their papers here, published between 2021 and 2024, ask how a single large network can be trained to produce fluent text in two languages, or to turn a written prompt into a picture or a moving clip. Recurring methods include converting images into sequences of discrete tokens, autoregressive and blank-infilling objectives, and, more recently, diffusion-based generation. The work sits mainly within [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/). The most cited of these papers is [GLM-130B](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/) (2022), which describes an openly released dense model of 130 billion parameters for English and Chinese. It departs from the decoder-only GPT design by using the GLM backbone, with bidirectional attention over visible context and training by filling in masked spans. The paper documents both successful and failed training choices. Earlier, [CogView](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/) (2021) treated general-domain text-to-image generation as a two-stage problem. A VQ-VAE-style tokenizer first maps each picture to a grid of codes, and a 4-billion-parameter transformer then models caption tokens followed by image tokens. That model was pretrained on roughly 30 million Chinese caption–image pairs. The newest work moves from still images to video. [CogVideoX](https://md.research.elamur.ai/free/en/papers/yang-2024-cogvideox-text-video-diffusion/) (2024) builds a diffusion transformer aimed at a familiar shortcoming of text-to-video systems. Their output clips tend to be brief, show little motion and follow prompts loosely when those prompts describe several events in order. Zheng's papers here are associated with [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/), with bylines from 2022 to 2024. Frequent co-authors include [Ming Ding](https://md.research.elamur.ai/free/en/authors/ming-ding/) and [Zhuoyi Yang](https://md.research.elamur.ai/free/en/authors/zhuoyi-yang/), each on three papers, along with [Wenyi Hong](https://md.research.elamur.ai/free/en/authors/wenyi-hong/) and [Da Yin](https://md.research.elamur.ai/free/en/authors/da-yin/). Other collaborators include [Aohan Zeng](https://md.research.elamur.ai/free/en/authors/aohan-zeng/) and [Jie Tang](https://md.research.elamur.ai/free/en/authors/jie-tang/). Wendi Zheng has 3 papers in our library, in 2021–2024, most of them on [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Ming Ding](https://md.research.elamur.ai/free/en/authors/ming-ding/), [Zhuoyi Yang](https://md.research.elamur.ai/free/en/authors/zhuoyi-yang/), [Da Yin](https://md.research.elamur.ai/free/en/authors/da-yin/). 3 papers in our library · 2021–2024 current affiliation [Tianjin University of Science and Technology](https://ror.org/018rbtf37), Tianjin, China; [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/), Beijing, China; [South China Sea Institute Of Oceanology](https://ror.org/0192yj155), Guangzhou, China; [Tianjin Institute of Industrial Biotechnology](https://ror.org/042pyga86), Tianjin, China; [State Key Laboratory of Tropical Oceanography](https://ror.org/02b5xr067), Guangzhou [OpenAlex](https://openalex.org/A5040179606) · [ORCID](https://orcid.org/0009-0008-7313-839X) · [**h-index 18**](https://openalex.org/authors/A5040179606) · [78 works](https://openalex.org/works?filter=author.id:A5040179606) · [1 474 citations](https://openalex.org/works?filter=author.id:A5040179606&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [3 works in our library](https://md.research.elamur.ai/free/en/authors/wendi-zheng/#papers) · [769 citations in our library](https://md.research.elamur.ai/free/en/authors/wendi-zheng/cited-by/) · 1 more work in our library · [⭐ Paper Star rank #1589](https://md.research.elamur.ai/free/en/paper-stars/page/16/#star-1589) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Wendi+Zheng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Wendi+Zheng) · [dblp](https://dblp.org/search?q=Wendi+Zheng) · [ACL Anthology](https://aclanthology.org/search/?q=Wendi+Zheng) ## Affiliations - [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) · Beijing, China —current · 2019–2026 on 15 papers - [South China Sea Institute Of Oceanology](https://ror.org/0192yj155) · Guangzhou, China —current · 2019–2026 on 13 papers - [State Key Laboratory of Tropical Oceanography](https://ror.org/02b5xr067) · Guangzhou —current · 2019–2026 on 13 papers - [Tianjin Institute of Industrial Biotechnology](https://ror.org/042pyga86) · Tianjin, China —current · 2026 on 1 paper - [Tianjin University of Science and Technology](https://ror.org/018rbtf37) · Tianjin, China —current · 2026 on 1 paper - [Fuzhou University](https://ror.org/011xvna82) · Fuzhou, China —2010–2025 on 35 papers - [The Affiliated Yongchuan Hospital of Chongqing Medical University](https://ror.org/033vnzz93) · Chongqing, China —2025 on 3 papers - [Henan Provincial People's Hospital](https://ror.org/03f72zw41) · Zhengzhou, China —2020–2024 on 8 papers - [Nanchang University](https://ror.org/042v6xz23) · Nanchang, China —2019–2024 on 7 papers - [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) · Beijing, China —2022–2024 on 4 papers From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 12 more institutions on single bylines are not listed. ## Topics [Vision & language 1](https://md.research.elamur.ai/free/en/topics/multimodal/) ## Works by year ### 2020s [**2024** 1 paper](https://md.research.elamur.ai/free/en/papers/yang-2024-cogvideox-text-video-diffusion/) · [**2022** 1 paper](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/) ## Papers The most cited first (OpenAlex and our library). 1. **[GLM-130B: An Open Bilingual Pre-trained Model](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)** *(2022 · 775 citations)* Few openly available models at the 100B-parameter scale matched GPT-3, and the methods for training them were mostly undisclosed. 2. **[CogView: Mastering Text-to-Image Generation via Transformers](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/)** *(2021 · 446 citations)* CogView addresses general-domain text-to-image generation. Earlier GAN-based systems did reasonably well on narrow datasets such as CUB but struggled with complex scenes like those in MS COCO. The authors frame the task as maximizing a variational bound on the joint likelihood of text and image. Training happens in… 3. **[CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://md.research.elamur.ai/free/en/papers/yang-2024-cogvideox-text-video-diffusion/)** *(2024 · 163 citations)* CogVideoX addresses a persistent weakness of text-to-video systems: clips that are short, nearly static, and poorly matched to prompts describing a sequence of events. The authors build a diffusion transformer around four main components: ## Also in our library 1 more work of theirs is in our library without a page here yet; it will appear as we publish them. ## Co-authors here 12 of 21 co-authors with a page, the most papers together first. - [Ming Ding](https://md.research.elamur.ai/free/en/authors/ming-ding/) ×3 - [Zhuoyi Yang](https://md.research.elamur.ai/free/en/authors/zhuoyi-yang/) ×3 - [Da Yin](https://md.research.elamur.ai/free/en/authors/da-yin/) ×2 - [Wenyi Hong](https://md.research.elamur.ai/free/en/authors/wenyi-hong/) ×2 - [Aohan Zeng](https://md.research.elamur.ai/free/en/authors/aohan-zeng/) - [Chang Zhou](https://md.research.elamur.ai/free/en/authors/chang-zhou/) - [Guanyu Feng](https://md.research.elamur.ai/free/en/authors/guanyu-feng/) - [Hanyu Lai](https://md.research.elamur.ai/free/en/authors/hanyu-lai/) - [Hongxia Yang](https://md.research.elamur.ai/free/en/authors/hongxia-yang/) - [Jiazheng Xu](https://md.research.elamur.ai/free/en/authors/jiazheng-xu/) - [Jie Tang](https://md.research.elamur.ai/free/en/authors/jie-tang/) - [Junyang Lin](https://md.research.elamur.ai/free/en/authors/junyang-lin/) ## Cited by Papers of our library cite their works 769 times (of the 140 884 papers whose bibliographies we read); [72 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/wendi-zheng/cited-by/).