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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.mova.elamur.ai/free/en/docs/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)

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

1. [Evaluating Discriminability of Vision-Language Models](https://aclanthology.org/2026.lrec-1.736/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
2. [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)*
3. [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*
4. [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)*
5. [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*
6. [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*
7. [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)*
8. [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*
9. [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)*
10. [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)*
11. [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*
12. [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)*
13. [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*
14. [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://arxiv.org/abs/2407.07895) *— 2024 · arXiv:2407.07895*
15. [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) *— 2024 · arXiv:2408.03326*
16. [MANTIS: Interleaved Multi-Image Instruction Tuning](https://arxiv.org/abs/2405.01483) *— 2024 · arXiv:2405.01483 · Transactions on Machine Learning Research 2024*
17. [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*
18. [PaliGemma: A versatile 3B VLM for transfer](https://arxiv.org/abs/2407.07726) *— 2024 · arXiv:2407.07726*
19. [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*
20. [VISREAS: Complex Visual Reasoning with Unanswerable Questions](https://aclanthology.org/2024.findings-acl.402/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
21. [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)*
22. [What matters when building vision-language models?](https://arxiv.org/abs/2405.02246) *— 2024 · arXiv:2405.02246*
23. [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)*
24. [mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models](https://arxiv.org/abs/2408.04840) *— 2024 · arXiv:2408.04840*
25. [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)*
26. [An Empirical Study of Multimodal Model Merging](https://aclanthology.org/2023.findings-emnlp.105/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
27. [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)*
28. [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*
29. [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)*
30. [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*
31. [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*
32. [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)*
33. [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*
34. [MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning](https://arxiv.org/abs/2309.07915) *— 2023 · arXiv:2309.07915*
35. [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)*
36. [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)*
37. [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)*
38. [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*
39. [Natural Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) *— 2023 · arXiv:2303.14725*
40. [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*
41. [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)*
42. [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*
43. [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*
44. [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*
45. [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)*
46. [Vision-Language Models for Vision Tasks: A Survey](https://arxiv.org/abs/2304.00685) *— 2023 · arXiv:2304.00685*
47. [Visual Spatial Reasoning](https://aclanthology.org/2023.tacl-1.37/) *— 2023*
48. [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*
49. [Abstract Visual Reasoning with Tangram Shapes](https://aclanthology.org/2022.emnlp-main.38/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
50. [Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation](https://aclanthology.org/2022.findings-acl.291/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
51. [AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models](https://aclanthology.org/2022.starsem-1.5/) *— 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics*
52. [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) *— 2022 · arXiv:2201.12086*
53. [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://arxiv.org/abs/2205.01917) *— 2022 · arXiv:2205.01917*
54. [Distilled Dual-Encoder Model for Vision-Language Understanding](https://aclanthology.org/2022.emnlp-main.608/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
55. [Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?](https://aclanthology.org/2022.emnlp-main.100/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
56. [Finding Structural Knowledge in Multimodal-BERT](https://aclanthology.org/2022.acl-long.388/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
57. [ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition](https://aclanthology.org/2022.naacl-main.232/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
58. [Image Retrieval from Contextual Descriptions](https://aclanthology.org/2022.acl-long.241/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis](https://aclanthology.org/2022.emnlp-main.659/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
60. [Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing](https://aclanthology.org/2022.acl-long.525/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
61. [Multilingual Multimodal Learning with Machine Translated Text](https://aclanthology.org/2022.findings-emnlp.308/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
62. [Named Entity and Relation Extraction with Multi-Modal Retrieval](https://aclanthology.org/2022.findings-emnlp.437/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
63. [Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues](https://aclanthology.org/2022.acl-long.66/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
64. [Probing the Role of Positional Information in Vision-Language Models](https://aclanthology.org/2022.findings-naacl.77/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
65. [SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training](https://arxiv.org/abs/2211.11446) *— 2022 · arXiv:2211.11446*
66. [Semantically Distributed Robust Optimization for Vision-and-Language Inference](https://aclanthology.org/2022.findings-acl.118/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
67. [TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection](https://aclanthology.org/2022.emnlp-main.273/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
68. [VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena](https://aclanthology.org/2022.acl-long.567/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
69. [Visual Programming: Compositional visual reasoning without training](https://arxiv.org/abs/2211.11559) *— 2022 · arXiv:2211.11559*
70. [mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections](https://aclanthology.org/2022.emnlp-main.488/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
71. [xGQA: Cross-Lingual Visual Question Answering](https://aclanthology.org/2022.findings-acl.196/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
72. [Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.162/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
73. [COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images](https://aclanthology.org/2021.emnlp-main.774/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
74. [Caption Enriched Samples for Improving Hateful Memes Detection](https://aclanthology.org/2021.emnlp-main.738/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
75. [Constrained Language Models Yield Few-Shot Semantic Parsers](https://aclanthology.org/2021.emnlp-main.608/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
76. [Data Efficient Masked Language Modeling for Vision and Language](https://aclanthology.org/2021.findings-emnlp.259/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
77. [DeCEMBERT: Learning from Noisy Instructional Videos via Dense Captions and Entropy Minimization](https://aclanthology.org/2021.naacl-main.193/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
78. [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://aclanthology.org/2021.tacl-1.21/) *— 2021*
79. [Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation](https://aclanthology.org/2021.acl-long.158/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
80. [Grounding `Grounding' in NLP](https://aclanthology.org/2021.findings-acl.375/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
81. [Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text](https://aclanthology.org/2021.emnlp-main.141/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
82. [Investigating Negation in Pre-trained Vision-and-language Models](https://aclanthology.org/2021.blackboxnlp-1.27/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
83. [Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering](https://aclanthology.org/2021.acl-long.564/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
84. [Multi-stage Pre-training over Simplified Multimodal Pre-training Models](https://aclanthology.org/2021.acl-long.199/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
85. [Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs](https://aclanthology.org/2021.tacl-1.58/) *— 2021 · Transactions of the Association for Computational Linguistics*
86. [Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge](https://aclanthology.org/2021.lantern-1.1/) *— 2021 · Proceedings of the Third Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)*
87. [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.mova.elamur.ai/free/en/docs/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/) *— our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
88. [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://arxiv.org/abs/2108.10904) *— 2021 · arXiv:2108.10904*
89. [TAPAS at SemEval-2021 Task 9: Reasoning over tables with intermediate pre-training](https://aclanthology.org/2021.semeval-1.51/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
90. [Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions](https://aclanthology.org/2021.naacl-main.420/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
91. [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) *— 2021 · arXiv:2102.03334*
92. [Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization](https://aclanthology.org/2021.emnlp-main.326/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
93. [Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning](https://aclanthology.org/2021.semeval-1.180/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
94. [Achieving Common Ground in Multi-modal Dialogue](https://aclanthology.org/2020.acl-tutorials.3/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts*
95. [Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision](https://aclanthology.org/2020.findings-emnlp.248/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
96. [Benchmarking Multimodal Regex Synthesis with Complex Structures](https://aclanthology.org/2020.acl-main.541/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
97. [Break It Down: A Question Understanding Benchmark](https://aclanthology.org/2020.tacl-1.13/) *— 2020 · Transactions of the Association for Computational Linguistics*
98. [CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning](https://aclanthology.org/2020.acl-main.682/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
99. [Cross-Modality Relevance for Reasoning on Language and Vision](https://aclanthology.org/2020.acl-main.683/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
100. [Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!](https://aclanthology.org/2020.emnlp-main.62/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*

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