---
title: "TechING — paper by Nadeem et al. (2026) — summary and ideas"
description: "They define primary tasks (Image2Code, Image2Description, Description2Code, ImageEnhancement-via-Prompt) and… 52 references."
authors: ["Tafazzul Nadeem", "Bhavik Shangari", "Manish Rai", "Gagan Raj Gupta", "Ashutosh Modi"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.142/"
doi: "10.18653/v1/2026.findings-eacl.142"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nadeem-2026-teching-real-world-technical-image/"
---
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# TechING: Towards Real World Technical Image Understanding via VLMs

**Authors:** Tafazzul Nadeem, Bhavik Shangari, Manish Rai, Gagan Raj Gupta and [Ashutosh Modi](https://md.research.elamur.ai/free/en/authors/ashutosh-modi/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.142/) · [PDF](https://aclanthology.org/2026.findings-eacl.142.pdf) · [DOI 10.18653/v1/2026.findings-eacl.142](https://doi.org/10.18653/v1/2026.findings-eacl.142) · [OpenAlex](https://openalex.org/W7140125536)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
Engineers often sketch flowcharts, class diagrams and similar technical drawings by hand, but turning such sketches into editable, machine-readable form usually means redrawing them. Current VLMs handle these images poorly, and large collections of real hand-drawn diagrams paired with code are expensive to build. The authors address this low-resource problem with TechING, a programmatically generated synthetic corpus. Each sample links a diagram image, its Mermaid code and a textual description. A separate set of human-made hand-drawn sketches is used only for evaluation. They define primary tasks (Image2Code, Image2Description, Description2Code, ImageEnhancement-via-Prompt) and self-supervision tasks (CodeCompletion, Image-Code similarity). They then fine-tune Llama 3.2 11B-instruct with LoRA to obtain LLama-VL-TUG. On synthetic data, the model clearly beats its base model and gives the strongest overall results among the baselines, which include open models of about 10B parameters and GPT-4o-mini. Human evaluation on real sketches confirms these gains. The work shows that synthetic data can transfer to messy real-world diagrams, which supports diagram-editing tools.

## Key points
- TechING contains 115,014 samples spanning 8 kinds of diagrams: flowcharts, sequence, state, class, C4, block, packet and graph diagrams. Synthetic data has easy, medium and hard levels, based on the number of components. A further 545 hand-drawn whiteboard and paper diagrams with Mermaid code serve as the real-world test set.
- Diagram content comes from discipline-specific keyword lists generated with GPT-4o and manually screened. Components, edges and labels are sampled uniformly at random.
- LLama-VL-TUG raises the ROUGE-L of Llama 3.2 11B-instruct by 2.14x. On real images, it improves average F1 by 6.97x and has the fewest compilation errors in 7 of 8 diagram types.
- Baseline VLMs mostly succeed only on graph-like diagrams, perform weakly on labeled edges and fail to produce packet diagrams. Only the fine-tuned model generates packet diagrams.
- The data is released at huggingface.co/datasets/Exploration-Lab/TechING and the model at Exploration-Lab/LLama-VL-TUG. Human evaluation was limited to diagrams with roughly 10–15 blocks.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Tafazzul Nadeem, Bhavik Shangari, Manish Rai, Gagan Raj Gupta and Ashutosh Modi. 2026. TechING: Towards Real World Technical Image Understanding via VLMs. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nadeem2026teching,
  title = {{TechING: Towards Real World Technical Image Understanding via VLMs}},
  author = {Tafazzul Nadeem, Bhavik Shangari, Manish Rai, Gagan Raj Gupta and Ashutosh Modi},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.142},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.142}
}
```

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## References

52 references, 20 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Kriti Aggarwal, Aditi Khandelwal, Kumar Tanmay, Owais Khan Mohammed, Qiang Liu, Monojit Choudhury, Hardik Chauhan, Subhojit Som, Vishrav Chaudhary, and Saurabh Tiwary. 2023. Dublin: Visual document understanding by language-image network. In Proceedings of the 2023 Conference on Empirical Methods in… [find](https://scholar.google.com/scholar?q=Kriti+Aggarwal%2C+Aditi+Khandelwal%2C+Kumar+Tanmay%2C+Owais+Khan+Mohammed%2C+Qiang+Liu%2C+Monojit+Choudhury%2C+Hardik+Chauhan%2C+Subhojit+Som%2C+Vishrav+Chaudhary%2C+and+Saurabh+Tiwary%2E+2023%2E+Dublin%3A+Visual+document+un)
2. Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, and Peter Anderson. 2019. nocaps: novel object captioning at scale. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE. [find](https://scholar.google.com/scholar?q=Harsh+Agrawal%2C+Karan+Desai%2C+Yufei+Wang%2C+Xinlei+Chen%2C+Rishabh+Jain%2C+Mark+Johnson%2C+Dhruv+Batra%2C+Devi+Parikh%2C+Stefan+Lee%2C+and+Peter+Anderson%2E+2019%2E+nocaps%3A+novel+object+captioning+at+scale%2E+In+2019+IEEE%2F)
3. Qihang Ai, Jiafan Li, Jincheng Dai, Jianwu Zhou, Lemao Liu, Haiyun Jiang, and Shuming Shi. 2024. Advancement in graph understanding: A multimodal benchmark and fine-tuning of vision-language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1:… [find](https://scholar.google.com/scholar?q=Qihang+Ai%2C+Jiafan+Li%2C+Jincheng+Dai%2C+Jianwu+Zhou%2C+Lemao+Liu%2C+Haiyun+Jiang%2C+and+Shuming+Shi%2E+2024%2E+Advancement+in+graph+understanding%3A+A+multimodal+benchmark+and+fine%2Dtuning+of+vision%2Dlanguage+models%2E+I)
4. Aishwarya Kamath. 2025. Gemma 3 technical report. [find](https://scholar.google.com/scholar?q=Aishwarya+Kamath%2E+2025%2E+Gemma+3+technical+report%2E)
5. Ahmad Montaser Awal, Guihuan Feng, Harold Mouchère, and Christian Viard-Gaudin. 2011. First experiments on a new online handwritten flowchart database. volume 7874, pages 1–10. [find](https://scholar.google.com/scholar?q=Ahmad+Montaser+Awal%2C+Guihuan+Feng%2C+Harold+Mouch%C3%A8re%2C+and+Christian+Viard%2DGaudin%2E+2011%2E+First+experiments+on+a+new+online+handwritten+flowchart+database%2E+volume+7874%2C+pages+1%E2%80%9310%2E)
6. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. [Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/).
7. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Ha… — [Qwen2.5-VL Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
8. Satanjeev Banerjee and Alon Lavie. 2005. [METEOR: An automatic metric for MT evaluation with improved correlation with human judgments](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/). In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pages 65–72, Ann Arbor, Michigan. Ass…
9. Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey Gritsenko, Neil Houlsby, Manoj Kumar, Ke… [find](https://scholar.google.com/scholar?q=Lucas+Beyer%2C+Andreas+Steiner%2C+Andr%C3%A9+Susano+Pinto%2C+Alexander+Kolesnikov%2C+Xiao+Wang%2C+Daniel+Salz%2C+Maxim+Neumann%2C+Ibrahim+Alabdulmohsin%2C+Michael+Tschannen%2C+Emanuele+Bugliarello%2C+Thomas+Unterthiner%2C+Danie)
10. Shreyanshu Bhushan, Eun-Soo Jung, and Minho Lee. 2024. Unveiling the power of integration: Block diagram summarization through local-global fusion. In Findings of the Association for Computational Linguistics: ACL 2024, pages 13837–13856, Bangkok, Thailand. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Shreyanshu+Bhushan%2C+Eun%2DSoo+Jung%2C+and+Minho+Lee%2E+2024%2E+Unveiling+the+power+of+integration%3A+Block+diagram+summarization+through+local%2Dglobal+fusion%2E+In+Findings+of+the+Association+for+Computational+Lin)
11. Shreyanshu Bhushan and Minho Lee. 2022. Block diagram-to-text: Understanding block diagram images by generating natural language descriptors. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 153–168, Online only. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Shreyanshu+Bhushan+and+Minho+Lee%2E+2022%2E+Block+diagram%2Dto%2Dtext%3A+Understanding+block+diagram+images+by+generating+natural+language+descriptors%2E+In+Findings+of+the+Association+for+Computational+Linguisti)
12. Xiaohui Chen, Satya Narayan Shukla, Mahmoud Azab, Aashu Singh, Qifan Wang, David Yang, ShengYun Peng, Hanchao Yu, Shen Yan, Xuewen Zhang, and Baosheng He. 2024. Compcap: Improving multimodal large language models with composite captions. [find](https://scholar.google.com/scholar?q=Xiaohui+Chen%2C+Satya+Narayan+Shukla%2C+Mahmoud+Azab%2C+Aashu+Singh%2C+Qifan+Wang%2C+David+Yang%2C+ShengYun+Peng%2C+Hanchao+Yu%2C+Shen+Yan%2C+Xuewen+Zhang%2C+and+Baosheng+He%2E+2024%2E+Compcap%3A+Improving+multimodal+large+lan)
13. Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, and C. Lawrence Zitnick. 2015. [Microsoft coco captions: Data collection and evaluation server](https://md.research.elamur.ai/free/en/papers/chen-2015-microsoft-coco-captions-data-collection/).
14. Zhi-Qi Cheng, Qi Dai, and Alexander G. Hauptmann. 2023. Chartreader: A unified framework for chart derendering and comprehension without heuristic rules. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 22202–22213. [find](https://scholar.google.com/scholar?q=Zhi%2DQi+Cheng%2C+Qi+Dai%2C+and+Alexander+G%2E+Hauptmann%2E+2023%2E+Chartreader%3A+A+unified+framework+for+chart+derendering+and+comprehension+without+heuristic+rules%2E+In+Proceedings+of+the+IEEE%2FCVF+International+C)
15. Claudio Filipi Goncalves dos Sants, Felype de Castro Bastos, Ana Claudia Akemi Matsuki de Faria, Jose Victor Nogueira Alves da Silva, Valeska de Sousa Uchoa, and Decio Goncalves de Aguiar Neto. 2023. Visual question answering: A survey on techniques and common trends in recent literature. [find](https://scholar.google.com/scholar?q=Claudio+Filipi+Goncalves+dos+Sants%2C+Felype+de+Castro+Bastos%2C+Ana+Claudia+Akemi+Matsuki+de+Faria%2C+Jose+Victor+Nogueira+Alves+da+Silva%2C+Valeska+de+Sousa+Uchoa%2C+and+Decio+Goncalves+de+Aguiar+Neto%2E+2023%2E+)
16. Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017. [Making the v in vqa matter: Elevating the role of image understanding in visual question answering](https://md.research.elamur.ai/free/en/papers/goyal-2016-making-v-vqa-matter-elevating/).
17. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. [Lora: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/).
18. Drew A. Hudson and Christopher D. Manning. 2019. [Gqa: A new dataset for real-world visual reasoning and compositional question answering](https://md.research.elamur.ai/free/en/papers/hudson-2019-gqa-new-dataset-real-world/).
19. Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Akos Kadar, Adam Trischler, and Yoshua Bengio. 2018. Figureqa: An annotated figure dataset for visual reasoning. [find](https://scholar.google.com/scholar?q=Samira+Ebrahimi+Kahou%2C+Vincent+Michalski%2C+Adam+Atkinson%2C+Akos+Kadar%2C+Adam+Trischler%2C+and+Yoshua+Bengio%2E+2018%2E+Figureqa%3A+An+annotated+figure+dataset+for+visual+reasoning%2E)
20. Aniruddha Kembhavi, Michael Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. 2016. [A diagram is worth a dozen images](https://md.research.elamur.ai/free/en/papers/kembhavi-2016-diagram-worth-dozen-images/). ArXiv, abs/1603.07396.
21. Jia Li, Lijie Hu, Zhixian He, Jingfeng Zhang, Tianhang Zheng, and Di Wang. 2024a. Text guided image editing with automatic concept locating and forgetting. [find](https://scholar.google.com/scholar?q=Jia+Li%2C+Lijie+Hu%2C+Zhixian+He%2C+Jingfeng+Zhang%2C+Tianhang+Zheng%2C+and+Di+Wang%2E+2024a%2E+Text+guided+image+editing+with+automatic+concept+locating+and+forgetting%2E)
22. Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. [Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models](https://md.research.elamur.ai/free/en/papers/li-2023-blip-2-bootstrapping-language-image/).
23. Xin Li, Yunfei Wu, Xinghua Jiang, Zhihao Guo, Mingming Gong, Haoyu Cao, Yinsong Liu, Deqiang Jiang, and Xing Sun. 2024b. Enhancing visual document understanding with contrastive learning in large visual-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni… [find](https://scholar.google.com/scholar?q=Xin+Li%2C+Yunfei+Wu%2C+Xinghua+Jiang%2C+Zhihao+Guo%2C+Mingming+Gong%2C+Haoyu+Cao%2C+Yinsong+Liu%2C+Deqiang+Jiang%2C+and+Xing+Sun%2E+2024b%2E+Enhancing+visual+document+understanding+with+contrastive+learning+in+large+visu)
24. Xue Li, Yiyou Sun, Wei Cheng, Yinglun Zhu, and Haifeng Chen. 2025. Chain-of-region: Visual language models need details for diagram analysis. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xue+Li%2C+Yiyou+Sun%2C+Wei+Cheng%2C+Yinglun+Zhu%2C+and+Haifeng+Chen%2E+2025%2E+Chain%2Dof%2Dregion%3A+Visual+language+models+need+details+for+diagram+analysis%2E+In+The+Thirteenth+International+Conference+on+Learning+Rep)
25. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
26. Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al. 2024. [Mmbench: Is your multi-modal model an all-around player?](https://md.research.elamur.ai/free/en/papers/liu-2023-mmbench-multi-modal-model-all/) In European conference on computer vision, pages 216–233. Springer.
27. Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, and Chong Ruan. 2024. [Deepseek-vl: Towards real-world vision-language understanding](https://md.research.elamur.ai/free/en/papers/lu-2024-deepseek-vl-real-world/).
28. Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. 2022. [Chartqa: A benchmark for question answering about charts with visual and logical reasoning](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/).
29. Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, and Pratyush Kumar. 2020. Plotqa: Reasoning over scientific plots. [find](https://scholar.google.com/scholar?q=Nitesh+Methani%2C+Pritha+Ganguly%2C+Mitesh+M%2E+Khapra%2C+and+Pratyush+Kumar%2E+2020%2E+Plotqa%3A+Reasoning+over+scientific+plots%2E)
30. Stephan Oepen, Omri Abend, Jan Hajic, Daniel Hershcovich, Marco Kuhlmann, Tim O’Gorman, Nianwen Xue, Jayeol Chun, Milan Straka, and Zdenka Uresova. 2019. [find](https://scholar.google.com/scholar?q=Stephan+Oepen%2C+Omri+Abend%2C+Jan+Hajic%2C+Daniel+Hershcovich%2C+Marco+Kuhlmann%2C+Tim+O%E2%80%99Gorman%2C+Nianwen+Xue%2C+Jayeol+Chun%2C+Milan+Straka%2C+and+Zdenka+Uresova%2E+2019%2E)
31. MRP 2019: Cross-framework meaning representation parsing. In Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning, pages 1–27, Hong Kong. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=MRP+2019%3A+Cross%2Dframework+meaning+representation+parsing%2E+In+Proceedings+of+the+Shared+Task+on+Cross%2DFramework+Meaning+Representation+Parsing+at+the+2019+Conference+on+Natural+Language+Learning%2C+pages)
32. OpenAI. 2024a. Gpt-4o: Advancing multimodal intelligence. Accessed: 2025-04-14. [find](https://scholar.google.com/scholar?q=OpenAI%2E+2024a%2E+Gpt%2D4o%3A+Advancing+multimodal+intelligence%2E+Accessed%3A+2025%2D04%2D14%2E)
33. OpenAI. 2024b. Gpt-4o mini: advancing cost-efficient intelligence. Accessed: 2025-04-14. [find](https://scholar.google.com/scholar?q=OpenAI%2E+2024b%2E+Gpt%2D4o+mini%3A+advancing+cost%2Defficient+intelligence%2E+Accessed%3A+2025%2D04%2D14%2E)
34. Huitong Pan, Qi Zhang, Cornelia Caragea, Eduard Dragut, and Longin Jan Latecki. 2024. Flowlearn: Evaluating large vision-language models on flowchart understanding. [find](https://scholar.google.com/scholar?q=Huitong+Pan%2C+Qi+Zhang%2C+Cornelia+Caragea%2C+Eduard+Dragut%2C+and+Longin+Jan+Latecki%2E+2024%2E+Flowlearn%3A+Evaluating+large+vision%2Dlanguage+models+on+flowchart+understanding%2E)
35. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational…
36. Maja Popović. 2015. [chrF: character n-gram F-score for automatic MT evaluation](https://md.research.elamur.ai/free/en/papers/popovic-2015-chrf-character-n-gram-f/). In Proceedings of the Tenth Workshop on Statistical Machine Translation, pages 392–395, Lisbon, Portugal. Association for Computational Linguistics.
37. Matt Post. 2018. [A call for clarity in reporting BLEU scores](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/). In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186– 191, Brussels, Belgium. Association for Computational Linguistics.
38. Juan A. Rodriguez, Abhay Puri, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, Sai Rajeswar, David Vazquez, Christopher Pal, and Marco Pedersoli. 2024. Starvector: Generating scalable vector graphics code from images and text. [find](https://scholar.google.com/scholar?q=Juan+A%2E+Rodriguez%2C+Abhay+Puri%2C+Shubham+Agarwal%2C+Issam+H%2E+Laradji%2C+Pau+Rodriguez%2C+Sai+Rajeswar%2C+David+Vazquez%2C+Christopher+Pal%2C+and+Marco+Pedersoli%2E+2024%2E+Starvector%3A+Generating+scalable+vector+graphic)
39. Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. 2022. [A-okvqa: A benchmark for visual question answering using world knowledge](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/).
40. Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020. [BLEURT: Learning robust metrics for text generation](https://md.research.elamur.ai/free/en/papers/sellam-2020-bleurt-learning-robust-metrics-text/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7881–7892, Online. Association for Computational Linguistics.
41. Shreya Shukla, Prajwal Gatti, Yogesh Kumar, Vikash Yadav, and Anand Mishra. 2023. Towards making flowchart images machine interpretable. In Document Analysis and Recognition - ICDAR 2023, pages 505–521, Cham. Springer Nature Switzerland. [find](https://scholar.google.com/scholar?q=Shreya+Shukla%2C+Prajwal+Gatti%2C+Yogesh+Kumar%2C+Vikash+Yadav%2C+and+Anand+Mishra%2E+2023%2E+Towards+making+flowchart+images+machine+interpretable%2E+In+Document+Analysis+and+Recognition+%2D+ICDAR+2023%2C+pages+505%E2%80%9352)
42. Shubhankar Singh, Purvi Chaurasia, Yerram Varun, Pranshu Pandya, Vatsal Gupta, Vivek Gupta, and Dan Roth. 2024. FlowVQA: Mapping multimodal logic in visual question answering with flowcharts. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1330–1350, Bangkok, Thailand.… [find](https://scholar.google.com/scholar?q=Shubhankar+Singh%2C+Purvi+Chaurasia%2C+Yerram+Varun%2C+Pranshu+Pandya%2C+Vatsal+Gupta%2C+Vivek+Gupta%2C+and+Dan+Roth%2E+2024%2E+FlowVQA%3A+Mapping+multimodal+logic+in+visual+question+answering+with+flowcharts%2E+In+Findi)
43. Simon Tannert, Marcelo G. Feighelstein, Jasmina Bogojeska, Joseph Shtok, Assaf Arbelle, Peter W. J. Staar, Anika Schumann, Jonas Kuhn, and Leonid Karlinsky. 2023. FlowchartQA: The first large-scale benchmark for reasoning over flowcharts. In Proceedings of the 1st Workshop on Linguistic Insights fro… [find](https://scholar.google.com/scholar?q=Simon+Tannert%2C+Marcelo+G%2E+Feighelstein%2C+Jasmina+Bogojeska%2C+Joseph+Shtok%2C+Assaf+Arbelle%2C+Peter+W%2E+J%2E+Staar%2C+Anika+Schumann%2C+Jonas+Kuhn%2C+and+Leonid+Karlinsky%2E+2023%2E+FlowchartQA%3A+The+first+large%2Dscale+be)
44. Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penche… [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Aishwarya+Kamath%2C+Johan+Ferret%2C+Shreya+Pathak%2C+Nino+Vieillard%2C+Ramona+Merhej%2C+Sarah+Perrin%2C+Tatiana+Matejovicova%2C+Alexandre+Ram%C3%A9%2C+Morgane+Rivi%C3%A8re%2C+Louis+Rouillard%2C+Thomas+Mesnard%2C+Geoffrey)
45. Shaowei Wang, Lingling Zhang, Longji Zhu, Tao Qin, Kim-Hui Yap, Xinyu Zhang, and Jun Liu. 2024. Cog-dqa: Chain-of-guiding learning with large language models for diagram question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 13969– 1397… [find](https://scholar.google.com/scholar?q=Shaowei+Wang%2C+Lingling+Zhang%2C+Longji+Zhu%2C+Tao+Qin%2C+Kim%2DHui+Yap%2C+Xinyu+Zhang%2C+and+Jun+Liu%2E+2024%2E+Cog%2Ddqa%3A+Chain%2Dof%2Dguiding+learning+with+large+language+models+for+diagram+question+answering%2E+In+Proceed)
46. Jingxuan Wei, Cheng Tan, Qi Chen, Gaowei Wu, Siyuan Li, Zhangyang Gao, Linzhuang Sun, Bihui Yu, and Ruifeng Guo. 2025. From Words to Structured Visuals: A Benchmark and Framework for Text-to-Diagram Generation and Editing . In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR… [find](https://scholar.google.com/scholar?q=Jingxuan+Wei%2C+Cheng+Tan%2C+Qi+Chen%2C+Gaowei+Wu%2C+Siyuan+Li%2C+Zhangyang+Gao%2C+Linzhuang+Sun%2C+Bihui+Yu%2C+and+Ruifeng+Guo%2E+2025%2E+From+Words+to+Structured+Visuals%3A+A+Benchmark+and+Framework+for+Text%2Dto%2DDiagram+G)
47. Ronghuan Wu, Wanchao Su, and Jing Liao. 2024. Chat2svg: Vector graphics generation with large language models and image diffusion models. [find](https://scholar.google.com/scholar?q=Ronghuan+Wu%2C+Wanchao+Su%2C+and+Jing+Liao%2E+2024%2E+Chat2svg%3A+Vector+graphics+generation+with+large+language+models+and+image+diffusion+models%2E)
48. Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, Qianyu Chen, Huarong Zhou, Zhensheng Zou, Haoye Zhang, Shengding Hu, Zhi Zheng, Jie Zhou, Jie Cai, Xu Han, Guoyang Zeng, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2024. [Minicpm-v: A gpt-4…](https://md.research.elamur.ai/free/en/papers/yao-2024-minicpm-v-gpt-4v/)
49. Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics (TACL), 2:67–78. [find](https://scholar.google.com/scholar?q=Peter+Young%2C+Alice+Lai%2C+Micah+Hodosh%2C+and+Julia+Hockenmaier%2E+2014%2E+From+image+descriptions+to+visual+denotations%3A+New+similarity+metrics+for+semantic+inference+over+event+descriptions%2E+Transactions+of)
50. Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. 2024. [Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/). In Proceedings of the IEEE/CVF Conference on Computer Vision an…
51. Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019. From recognition to cognition: Visual commonsense reasoning. [find](https://scholar.google.com/scholar?q=Rowan+Zellers%2C+Yonatan+Bisk%2C+Ali+Farhadi%2C+and+Yejin+Choi%2E+2019%2E+From+recognition+to+cognition%3A+Visual+commonsense+reasoning%2E)
52. Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, In So Kweon, and Junmo Kim. 2024. Text-to-image diffusion models in generative ai: A survey. [find](https://scholar.google.com/scholar?q=Chenshuang+Zhang%2C+Chaoning+Zhang%2C+Mengchun+Zhang%2C+In+So+Kweon%2C+and+Junmo+Kim%2E+2024%2E+Text%2Dto%2Dimage+diffusion+models+in+generative+ai%3A+A+survey%2E)

*Updated 31 October 2026*