Probing Image-Language Transformers for Verb Understanding
Open accessAbstract
Multimodal image-language transformers have achieved impressive results on a variety of tasks that rely on fine-tuning (e.g., visual question answering and image retrieval).We are interested in shedding light on the quality of their pretrained representationsin particular, if these models can distinguish different types of verbs or if they rely solely on nouns in a given sentence.To do so, we collect a dataset of image-sentence pairs (in English) consisting of 421 verbs that are either visual or commonly found in the pretraining data (i.e., the Conceptual Captions dataset).We use this dataset to evaluate pretrained image-language transformers and find that they fail more in situations that require verb understanding compared to other parts of speech.We also investigate what category of verbs are particularly challenging.
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Human behaviours, emotions, events, actions, and humour are multimodal, so various human-centred multimodal learning tasks are widely studied.
“Furthermore, human behaviours, emotions, events, actions, and humour are multimodal, thus various human-centred MML tasks are widely studied, including multimodal emotion recognition [67], multimodal event representation [68], understanding multimodal humor [69], face-body-voice based video person-clustering [70], etc.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [67]
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References 33 · Cited by 63
Research works printed but not linked: 2
- Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata. 2018. Grounding visual explana- tions. In Proceedings of the European Conference on Computer Vision (ECCV), pages 264-279.
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Cor- rado, and Jeffrey Dean. 2013. Distributed represen- tations of words and phrases and their composition- ality. NeurIPS.
- Authors
- Hendricks, L. A., & Nematzadeh, A.
- ORCID
- Hendricks, Lisa Anne
- Venue
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Published
- 2021
- Pages
- 3635-3644
- OpenAlex
- W3177487519
- Cited by
- 63
- References
- 33
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 13 · 2023: 23 · 2022: 16 · 2021: 3
- FWCI
- 4.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- visual verbs, multimodal pre-training, visual grounding
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