@inproceedings{wazni-etal-2024-verbclip,
title = "{V}erb{CLIP}: Improving Verb Understanding in Vision-Language Models with Compositional Structures",
author = "Wazni, Hadi and
Lo, Kin and
Sadrzadeh, Mehrnoosh",
editor = "Gu, Jing and
Fu, Tsu-Jui (Ray) and
Hudson, Drew and
Celikyilmaz, Asli and
Wang, William",
booktitle = "Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.alvr-1.17",
pages = "195--201",
abstract = "Verbs describe the dynamics of interactions between people, objects, and their environments. They play a crucial role in language formation and understanding. Nonetheless, recent vision-language models like CLIP predominantly rely on nouns and have a limited account of verbs. This limitation affects their performance in tasks requiring action recognition and scene understanding. In this work, we introduce VerbCLIP, a verb-centric vision-language model which learns meanings of verbs based on a compositional approach to statistical machine learning. Our methods significantly outperform CLIP in zero-shot performance on the VALSE, VL-Checklist, and SVO-Probes datasets, with improvements of +2.38{\%}, +3.14{\%}, and +1.47{\%}, without fine-tuning. Fine-tuning resulted in further improvements, with gains of +2.85{\%} and +9.2{\%} on the VALSE and VL-Checklist datasets.",
}
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<abstract>Verbs describe the dynamics of interactions between people, objects, and their environments. They play a crucial role in language formation and understanding. Nonetheless, recent vision-language models like CLIP predominantly rely on nouns and have a limited account of verbs. This limitation affects their performance in tasks requiring action recognition and scene understanding. In this work, we introduce VerbCLIP, a verb-centric vision-language model which learns meanings of verbs based on a compositional approach to statistical machine learning. Our methods significantly outperform CLIP in zero-shot performance on the VALSE, VL-Checklist, and SVO-Probes datasets, with improvements of +2.38%, +3.14%, and +1.47%, without fine-tuning. Fine-tuning resulted in further improvements, with gains of +2.85% and +9.2% on the VALSE and VL-Checklist datasets.</abstract>
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%0 Conference Proceedings
%T VerbCLIP: Improving Verb Understanding in Vision-Language Models with Compositional Structures
%A Wazni, Hadi
%A Lo, Kin
%A Sadrzadeh, Mehrnoosh
%Y Gu, Jing
%Y Fu, Tsu-Jui (Ray)
%Y Hudson, Drew
%Y Celikyilmaz, Asli
%Y Wang, William
%S Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wazni-etal-2024-verbclip
%X Verbs describe the dynamics of interactions between people, objects, and their environments. They play a crucial role in language formation and understanding. Nonetheless, recent vision-language models like CLIP predominantly rely on nouns and have a limited account of verbs. This limitation affects their performance in tasks requiring action recognition and scene understanding. In this work, we introduce VerbCLIP, a verb-centric vision-language model which learns meanings of verbs based on a compositional approach to statistical machine learning. Our methods significantly outperform CLIP in zero-shot performance on the VALSE, VL-Checklist, and SVO-Probes datasets, with improvements of +2.38%, +3.14%, and +1.47%, without fine-tuning. Fine-tuning resulted in further improvements, with gains of +2.85% and +9.2% on the VALSE and VL-Checklist datasets.
%U https://aclanthology.org/2024.alvr-1.17
%P 195-201
Markdown (Informal)
[VerbCLIP: Improving Verb Understanding in Vision-Language Models with Compositional Structures](https://aclanthology.org/2024.alvr-1.17) (Wazni et al., ALVR-WS 2024)
ACL