Hadi Wazni


2024

pdf bib
VerbCLIP: Improving Verb Understanding in Vision-Language Models with Compositional Structures
Hadi Wazni | Kin Ian Lo | Mehrnoosh Sadrzadeh
Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)

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.

2023

pdf bib
Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach
Hadi Wazni | Mehrnoosh Sadrzadeh
Proceedings of The Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023)