---
title: "Who cites A Corpus for Reasoning About Natural Language…"
description: "The papers of the Elamur Research library that cite «A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/cited-by/"
---
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# Who cites «A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)»

192 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)

Pages: **1** · [2](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/cited-by/page/2/)

1. [CLEVR-3D-DeRef](https://aclanthology.org/2026.lrec-1.745/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
2. [Evaluating Discriminability of Vision-Language Models](https://aclanthology.org/2026.lrec-1.736/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
3. [ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence](https://arxiv.org/abs/2606.19538) *— 2026 · arXiv:2606.19538*
4. [Localizing Events in Space: Comparing Humans and AI Models](https://aclanthology.org/2026.lrec-1.865/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
5. [MiSCHiEF: A Benchmark in Minimal-Pairs of Safety and Culture for Holistic Evaluation of Fine-Grained Image-Caption Alignment](https://aclanthology.org/2026.eacl-short.29/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
6. [Mind”s Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs](https://aclanthology.org/2026.acl-long.2124/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [More Images, More Problems? A Controlled Analysis of VLM Failure Modes.](https://aclanthology.org/2026.findings-acl.366/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
8. [Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks](https://aclanthology.org/2026.findings-acl.1379/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
9. [On the Additive Compositionality of Task Vectors in Vision–Language Models](https://aclanthology.org/2026.eacl-short.38/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
10. [S2H-DPO: Hardness-Aware Preference Optimization for Vision–Language Models](https://aclanthology.org/2026.findings-acl.1825/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
11. [AbsVis – Benchmarking How Humans and Vision-Language Models “See” Abstract Concepts in Images](https://aclanthology.org/2025.emnlp-main.417/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
12. [Breaking Language Barriers in Visual Language Models via Multilingual Textual Regularization](https://aclanthology.org/2025.findings-ijcnlp.18/) *— 2025 · Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
13. [Burn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences?](https://aclanthology.org/2025.findings-acl.1248/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
14. [Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model](https://aclanthology.org/2025.acl-long.143/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [ColorFoil: Investigating Color Blindness in Large Vision and Language Models](https://aclanthology.org/2025.naacl-srw.29/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)*
16. [Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation](https://aclanthology.org/2025.findings-acl.1342/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
17. [Evaluation of Multilingual Image Captioning: How far can we get with CLIP models?](https://aclanthology.org/2025.findings-naacl.287/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
18. [GlyphPattern: An Abstract Pattern Recognition for Vision-Language Models](https://aclanthology.org/2025.findings-acl.63/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
19. [Looking Beyond the Pixels: Evaluating Visual Metaphor Understanding in VLMs](https://aclanthology.org/2025.findings-emnlp.1257/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
20. [M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG](https://arxiv.org/abs/2512.05959) *— 2025 · arXiv:2512.05959*
21. [MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale](https://aclanthology.org/2025.acl-long.680/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Multilingual Vision-Language Models, A Survey](https://arxiv.org/abs/2509.22123) *— 2025 · arXiv:2509.22123*
23. [Quantifying Memorization and Parametric Response Rates in Retrieval-Augmented Vision-Language Models](https://aclanthology.org/2025.l2m2-1.10/) *— 2025 · Proceedings of the First Workshop on Large Language Model Memorization (L2M2)*
24. [RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios](https://aclanthology.org/2025.findings-emnlp.1039/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
25. [TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language Models](https://aclanthology.org/2025.findings-acl.359/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
26. [The Artificial Intelligence Cognitive Examination: A Survey on the Evolution of Multimodal Evaluation from Recognition to Reasoning](https://arxiv.org/abs/2510.04141) *— 2025 · arXiv:2510.04141 · IEEE Access, vol. 14, Dec. 2025, Art. no. 3649182*
27. [Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains](https://aclanthology.org/2025.acl-long.1347/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [What Is Missing in Multilingual Visual Reasoning and How to Fix It](https://aclanthology.org/2025.findings-naacl.144/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
29. [An Efficient Post-hoc Framework for Reducing Task Discrepancy of Text Encoders for Composed Image Retrieval](https://arxiv.org/abs/2406.09188) *— 2024 · arXiv:2406.09188*
30. [Attribute Diversity Determines the Systematicity Gap in VQA](https://aclanthology.org/2024.emnlp-main.537/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
31. [Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion](https://aclanthology.org/2024.acl-long.605/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [Curriculum Masking in Vision-Language Pretraining to Maximize Cross Modal Interaction](https://aclanthology.org/2024.naacl-long.203/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
33. [DoRA: Weight-Decomposed Low-Rank Adaptation](https://arxiv.org/abs/2402.09353) *— 2024 · arXiv:2402.09353*
34. [EVil-Probe - a Composite Benchmark for Extensive Visio-Linguistic Probing](https://aclanthology.org/2024.lrec-main.591/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
35. [Evaluating In-Context Learning of Libraries for Code Generation](https://aclanthology.org/2024.naacl-long.161/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
36. [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://arxiv.org/abs/2412.05271) *— 2024 · arXiv:2412.05271*
37. [FRoG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in LLMs](https://aclanthology.org/2024.emnlp-main.411/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
38. [FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension](https://aclanthology.org/2024.emnlp-main.864/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
39. [Improving Vision-Language Cross-Lingual Transfer with Scheduled Unfreezing](https://aclanthology.org/2024.alvr-1.13/) *— 2024 · Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)*
40. [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://arxiv.org/abs/2407.07895) *— 2024 · arXiv:2407.07895*
41. [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) *— 2024 · arXiv:2408.03326*
42. [LightVLP: A Lightweight Vision-Language Pre-training via Gated Interactive Masked AutoEncoders](https://aclanthology.org/2024.lrec-main.918/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
43. [MANTIS: Interleaved Multi-Image Instruction Tuning](https://arxiv.org/abs/2405.01483) *— 2024 · arXiv:2405.01483 · Transactions on Machine Learning Research 2024*
44. [MIBench: Evaluating Multimodal Large Language Models over Multiple Images](https://aclanthology.org/2024.emnlp-main.1250/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
45. [Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals](https://aclanthology.org/2024.findings-emnlp.205/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
46. [MultiSkill: Evaluating Large Multimodal Models for Fine-grained Alignment Skills](https://aclanthology.org/2024.findings-emnlp.81/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
47. [PaliGemma: A versatile 3B VLM for transfer](https://arxiv.org/abs/2407.07726) *— 2024 · arXiv:2407.07726*
48. [Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models](https://aclanthology.org/2024.emnlp-main.947/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
49. [SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models](https://aclanthology.org/2024.lrec-main.1300/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
50. [SparseFlow: Accelerating Transformers by Sparsifying Information Flows](https://aclanthology.org/2024.acl-long.323/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
51. [Spatial and Temporal Language Understanding: Representation, Reasoning, and Grounding](https://aclanthology.org/2024.naacl-tutorials.6/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)*
52. [TAME-RD: Text Assisted Replication of Image Multi-Adjustments for Reverse Designing](https://aclanthology.org/2024.findings-acl.637/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
53. [TransferCVLM: Transferring Cross-Modal Knowledge for Vision-Language Modeling](https://aclanthology.org/2024.findings-emnlp.975/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
54. [VDebugger: Harnessing Execution Feedback for Debugging Visual Programs](https://aclanthology.org/2024.findings-emnlp.575/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
55. [VISREAS: Complex Visual Reasoning with Unanswerable Questions](https://aclanthology.org/2024.findings-acl.402/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
56. [Visual-Textual Entailment with Quantities Using Model Checking and Knowledge Injection](https://aclanthology.org/2024.lrec-main.1512/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
57. [What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?](https://aclanthology.org/2024.naacl-long.440/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
58. [What matters when building vision-language models?](https://arxiv.org/abs/2405.02246) *— 2024 · arXiv:2405.02246*
59. [mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs](https://aclanthology.org/2024.alvr-1.2/) *— 2024 · Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)*
60. [mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models](https://arxiv.org/abs/2408.04840) *— 2024 · arXiv:2408.04840*
61. [中文图文多模态理解评测](https://aclanthology.org/2024.ccl-3.42/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)*
62. [Accelerating Vision-Language Pretraining with Free Language Modeling](https://arxiv.org/abs/2303.14038) *— 2023 · arXiv:2303.14038*
63. [An Empirical Study of Multimodal Model Merging](https://aclanthology.org/2023.findings-emnlp.105/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
64. [Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training](https://aclanthology.org/2023.acl-long.315/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
65. [Cultural Concept Adaptation on Multimodal Reasoning](https://aclanthology.org/2023.emnlp-main.18/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
66. [Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond](https://aclanthology.org/2023.findings-emnlp.576/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
67. [Dealing with Semantic Underspecification in Multimodal NLP](https://aclanthology.org/2023.acl-long.675/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
68. [Debiasing Multimodal Models via Causal Information Minimization](https://aclanthology.org/2023.findings-emnlp.270/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
69. [EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning](https://aclanthology.org/2023.findings-acl.873/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
70. [Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models](https://aclanthology.org/2023.emnlp-main.525/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
71. [Fusion or Defusion? Flexible Vision-and-Language Pre-Training](https://aclanthology.org/2023.findings-acl.316/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
72. [Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks](https://aclanthology.org/2023.findings-acl.894/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
73. [LAVIS: A One-stop Library for Language-Vision Intelligence](https://aclanthology.org/2023.acl-demo.3/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
74. [MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting](https://aclanthology.org/2023.eacl-main.185/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
75. [MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning](https://arxiv.org/abs/2309.07915) *— 2023 · arXiv:2309.07915*
76. [ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning](https://aclanthology.org/2023.acl-long.811/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://aclanthology.org/2023.acl-long.714/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
78. [Measuring Progress in Fine-grained Vision-and-Language Understanding](https://aclanthology.org/2023.acl-long.87/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [Medical Visual Textual Entailment for Numerical Understanding of Vision-and-Language Models](https://aclanthology.org/2023.clinicalnlp-1.2/) *— 2023 · Proceedings of the 5th Clinical Natural Language Processing Workshop*
80. [Meta-learning For Vision-and-language Cross-lingual Transfer](https://aclanthology.org/2023.mrl-1.2/) *— 2023 · Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL)*
81. [Modular Visual Question Answering via Code Generation](https://aclanthology.org/2023.acl-short.65/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
82. [Multitask Multimodal Prompted Training for Interactive Embodied Task Completion](https://aclanthology.org/2023.emnlp-main.50/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
83. [Natural Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) *— 2023 · arXiv:2303.14725*
84. [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://aclanthology.org/2023.findings-emnlp.840/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
85. [Processing Referential Ambiguities in Situated Dialogue Systems](https://aclanthology.org/2023.yrrsds-1.1/) *— 2023 · Proceedings of the 19th Annual Meeting of the Young Reseachers' Roundtable on Spoken Dialogue Systems*
86. [PuMer: Pruning and Merging Tokens for Efficient Vision Language Models](https://aclanthology.org/2023.acl-long.721/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
87. [RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training](https://aclanthology.org/2023.findings-acl.746/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
88. [Scaling Vision-Language Models with Sparse Mixture of Experts](https://aclanthology.org/2023.findings-emnlp.758/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
89. [Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation](https://aclanthology.org/2023.findings-emnlp.466/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
90. [Text encoders bottleneck compositionality in contrastive vision-language models](https://aclanthology.org/2023.emnlp-main.301/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
91. [Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks](https://aclanthology.org/2023.findings-emnlp.40/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
92. [TraVLR: Now You See It, Now You Don”t! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoning](https://aclanthology.org/2023.eacl-main.242/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
93. [Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training](https://aclanthology.org/2023.acl-long.327/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
94. [Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation](https://aclanthology.org/2023.acl-long.819/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [Vision-Language Models for Vision Tasks: A Survey](https://arxiv.org/abs/2304.00685) *— 2023 · arXiv:2304.00685*
96. [Visual Spatial Reasoning](https://aclanthology.org/2023.tacl-1.37/) *— 2023*
97. [Weakly Supervised Vision-and-Language Pre-training with Relative Representations](https://aclanthology.org/2023.acl-long.464/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
98. [Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining](https://aclanthology.org/2023.emnlp-main.184/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
99. [What's “up” with vision-language models? Investigating their struggle with spatial reasoning](https://aclanthology.org/2023.emnlp-main.568/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
100. [lilGym: Natural Language Visual Reasoning with Reinforcement Learning](https://aclanthology.org/2023.acl-long.512/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*

Pages: **1** · [2](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/cited-by/page/2/)