A Diagram is Worth a Dozen Images
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Effective interpretation of diagrams requires identifying and reasoning about the relationships that connect symbols, shapes, and spatial groupings.
“Research on diagram understanding shows that effective interpretation goes beyond detecting individual visual elements. Comprehension requires identifying and reasoning about the relationships that connect symbols, shapes, and spatial groupings.”
From Human vs LLM-Generated Alt Text for STEM Image Accessibility: A Comparative Study (2026) · cites this paper as [12]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Self-citationVQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487 · see citing works
WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yang, Y., Yih, W.-T., & Meek, C. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1237 · Cited by 829 · see citing works
Visual7W: Grounded Question Answering in Images
Zhu, Y., Groth, O., Bernstein, M., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.540 · Cited by 794 · cited 1× · see citing works
“Recently, answering questions about real images has drawn much attention in both NLP and vision communities [22,23,24].”
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Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02283 · Cited by 734 · see citing works
Kembhavi, A., Seo, M., Schwenk, D., Choi, J., Farhadi, A., & Hajishirzi, H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.571 · Cited by 244 · see citing works
Self-citationBai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2511.21631 · Cited by 27 · see citing works
“On OCR-related visual question answering (VQA) benchmarks that require both OCR capability and keyword search — such as DocVQA (Mathew et al., 2021b), InfoVQA (Mathew et al., 2021a), AI2D (Kembhavi et al., 2016), ChartQA (Masry et al., 2022), and the CharXiv (Wang et al., 2024g) description subset — both the Instruct…”
InfluentialPrismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
Karamcheti, S., Nair, S., Balakrishna, A., Liang, P., Kollar, T., & Sadigh, D. (2024) · arXiv · doi:10.48550/arxiv.2402.07865 · Cited by 7
“…four tasks spanning object localization (Kazemzadeh et al., 2014; Yu et al., 2016; Wang et al., 2021), and four challenge tasks evaluating fine-grained spatial reasoning, hallucination, and diagram understanding (Acharya et al., 2018; Liu et al., 2022; Li et al., 2023d; Kembhavi et al., 2016).”
BackgroundMethodA Descriptive Review of Image Datasets for Accessible Alternative Descriptions in STEM Domains
Cardia, M., Buzzi, M., Galesi, G., & Leporini, B. (2025) · Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter · doi:10.1145/3750069.3750122 · Cited by 5 · see citing works
Human vs LLM-Generated Alt Text for STEM Image Accessibility: A Comparative Study
Cardia, M., Angileri, L., Poggianti, C., & Leporini, B. (2026) · Proceedings of the 23rd International Web for All Conference · doi:10.1145/3800424.3800457 · Cited by 2 · see citing works
“Kemb-havi et al. formalized this concept through Diagram Parse Graphs (DPGs) [12], proposing a framework that integrates visual recognition with structural parsing and symbolic reasoning.”
InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning
Han, X., Jian, Y., Hu, X., Liu, H., Wang, Y., Fan, Q., Ai, Y., Huang, H., He, R., Yang, Z., et al. (2024) · arXiv · doi:10.48550/arxiv.2409.12568 · Cited by 1 · see citing works
Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Arviv, O., Greenewald, K., Perlitz, Y., Mulian, H., Shmueli-Scheuer, M., & Choshen, L. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.43
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.05530 · Cited by 306 · see citing works
- Authors
- Kembhavi, A., Salvato, M., Kolve, E., Seo, M., Hajishirzi, H., & Farhadi, A.
- Affiliations
- Allen Institute for Artificial Intelligence (US)
- University of Washington (US)
- Corresponding author
- Kembhavi, Aniruddha
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 235-251
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2307512708
- Cited by
- 290
- References
- 5
- Citations by year
- 2026: 80 · 2025: 76 · 2024: 37 · 2023: 18 · 2022: 9 · 2021: 23 · 2020: 10 · 2019: 16 · 2018: 16 · 2017: 4 · 2016: 1
- FWCI
- 4.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- diagram understanding, syntactic analysis, attention-based models, LSTM, visual reasoning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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