5 citations · 20 across the 11 of their papers we have counts for
5 papers · 1 filter
LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models
Gabriela Ben Melech Stan, Estelle Aflalo, Raanan Yehezkel Rohekar +7
In the rapidly evolving landscape of artificial intelligence, multi-modal large language models are emerging as a significant area of interest. These models, which combine various…
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
Xiao Xu, Bei Li, Chenfei Wu +6
Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks. Although the most advanced work improves performance by building bridges bet…
MuMUR : Multilingual Multimodal Universal Retrieval
Avinash Madasu, Estelle Aflalo, Gabriela Ben Melech Stan +4
Multi-modal retrieval has seen tremendous progress with the development of vision-language models. However, further improving these models require additional labelled data which is…
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers
Estelle Aflalo, Meng Du, Shao-Yen Tseng +4
Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability t…
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation
Yongfei Liu, Chenfei Wu, Shao-yen Tseng +3
Self-supervised vision-and-language pretraining (VLP) aims to learn transferable multi-modal representations from large-scale image-text data and to achieve strong performances on…