---
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…"
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# Who cites «A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)» · page 2

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

1. [État des lieux des Transformers Vision-Langage : Un éclairage sur les données de pré-entraînement](https://aclanthology.org/2023.jeptalnrecital-statement.2/) *— 2023 · Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 3 : prises de position en TAL*
2. [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*
3. [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*
4. [An Anchor-based Relative Position Embedding Method for Cross-Modal Tasks](https://aclanthology.org/2022.emnlp-main.362/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
5. [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*
6. [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) *— 2022 · arXiv:2201.12086*
7. [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://arxiv.org/abs/2205.01917) *— 2022 · arXiv:2205.01917*
8. [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*
9. [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*
10. [End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching](https://aclanthology.org/2022.emnlp-main.742/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
11. [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)*
12. [Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems](https://aclanthology.org/2022.findings-emnlp.345/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
13. [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*
14. [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)*
15. [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*
16. [Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval](https://aclanthology.org/2022.naacl-main.300/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
17. [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)*
18. [Multilingual Multimodal Learning with Machine Translated Text](https://aclanthology.org/2022.findings-emnlp.308/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
19. [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*
20. [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)*
21. [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*
22. [SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training](https://arxiv.org/abs/2211.11446) *— 2022 · arXiv:2211.11446*
23. [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*
24. [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*
25. [Transformer-based Localization from Embodied Dialog with Large-scale Pre-training](https://aclanthology.org/2022.aacl-short.36/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
26. [Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions](https://aclanthology.org/2022.umios-1.6/) *— 2022 · Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)*
27. [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)*
28. [Visual Programming: Compositional visual reasoning without training](https://arxiv.org/abs/2211.11559) *— 2022 · arXiv:2211.11559*
29. [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*
30. [xGQA: Cross-Lingual Visual Question Answering](https://aclanthology.org/2022.findings-acl.196/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
31. [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*
32. [Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference](https://aclanthology.org/2021.mmsr-1.10/) *— 2021 · Proceedings of the 1st Workshop on Multimodal Semantic Representations (MMSR)*
33. [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*
34. [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*
35. [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*
36. [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*
37. [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*
38. [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://aclanthology.org/2021.tacl-1.21/) *— 2021*
39. [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)*
40. [Grounding `Grounding' in NLP](https://aclanthology.org/2021.findings-acl.375/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
41. [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*
42. [In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering](https://aclanthology.org/2021.acl-short.60/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
43. [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*
44. [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)*
45. [Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions](https://aclanthology.org/2021.emnlp-main.516/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
46. [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)*
47. [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*
48. [Probing Cross-Modal Representations in Multi-Step Relational Reasoning](https://aclanthology.org/2021.repl4nlp-1.16/) *— 2021 · Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)*
49. [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)*
50. [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.research.elamur.ai/free/en/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*
51. [Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables](https://aclanthology.org/2021.semeval-1.179/) *— 2021 · Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)*
52. [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://arxiv.org/abs/2108.10904) *— 2021 · arXiv:2108.10904*
53. [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)*
54. [Transformer-Exclusive Cross-Modal Representation for Vision and Language](https://aclanthology.org/2021.findings-acl.240/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
55. [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*
56. [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) *— 2021 · arXiv:2102.03334*
57. [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*
58. [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)*
59. [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*
60. [Adjusting Image Attributes of Localized Regions with Low-level Dialogue](https://aclanthology.org/2020.lrec-1.51/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
61. [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*
62. [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*
63. [Break It Down: A Question Understanding Benchmark](https://aclanthology.org/2020.tacl-1.13/) *— 2020 · Transactions of the Association for Computational Linguistics*
64. [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*
65. [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*
66. [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)*
67. [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
68. [Experience Grounds Language](https://aclanthology.org/2020.emnlp-main.703/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
69. [How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://aclanthology.org/2020.acl-main.465/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
70. [Learning to Represent Image and Text with Denotation Graph](https://aclanthology.org/2020.emnlp-main.60/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
71. [MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering](https://aclanthology.org/2020.emnlp-main.63/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
72. [New Protocols and Negative Results for Textual Entailment Data Collection](https://aclanthology.org/2020.emnlp-main.658/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
73. [Obtaining Faithful Interpretations from Compositional Neural Networks](https://aclanthology.org/2020.acl-main.495/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
74. [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://arxiv.org/abs/2004.06165) *— 2020 · arXiv:2004.06165*
75. [Profile Consistency Identification for Open-domain Dialogue Agents](https://aclanthology.org/2020.emnlp-main.539/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
76. [Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks](https://aclanthology.org/2020.emnlp-tutorials.5/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts*
77. [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://arxiv.org/abs/2005.04790) *— 2020 · arXiv:2005.04790*
78. [Understanding tables with intermediate pre-training](https://aclanthology.org/2020.findings-emnlp.27/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
79. [VD-BERT: A Unified Vision and Dialog Transformer with BERT](https://aclanthology.org/2020.emnlp-main.269/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
80. [What Does BERT with Vision Look At?](https://aclanthology.org/2020.acl-main.469/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
81. [X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers](https://aclanthology.org/2020.emnlp-main.707/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
82. [多模态视觉语言表征学习研究综述](https://jos.org.cn/jos/article/abstract/6125) *— 2020 · 软件学报 (Journal of Software)*
83. [Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets](https://aclanthology.org/D19-1107/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
84. [Expressing Visual Relationships via Language](https://aclanthology.org/P19-1182/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
85. [GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering](https://arxiv.org/abs/1902.09506) *— 2019 · arXiv:1902.09506*
86. [Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts](https://aclanthology.org/D19-1285/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
87. [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://aclanthology.org/D19-1514/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
88. [Neural Naturalist: Generating Fine-Grained Image Comparisons](https://aclanthology.org/D19-1065/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
89. [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164) *— 2019 · arXiv:1909.02164*
90. [UNITER: UNiversal Image-TExt Representation Learning](https://arxiv.org/abs/1909.11740) *— 2019 · arXiv:1909.11740*
91. [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/abs/1908.03557) *— 2019 · arXiv:1908.03557*
92. [Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments](https://arxiv.org/abs/1811.12354) *— 2018 · arXiv:1811.12354 · Published in CVPR 2019*

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