2 citations · 3 across the 8 of their papers we have counts for
9 papers
The Stochastic Shift: A New Evaluation Paradigm for Text-to-SQL with AI Operators
Tarfah Alrashed, Fatma Ozcan, Per Jacobsson +2
SQL has been augmented with AI operators, enabling modern data analytics platforms to derive insights from both structured and unstructured data. We observe that while current Text…
Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs
Jinqi Luo, Jinyu Yang, Tal Neiman +5
Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prompt engineering, response class…
M-LLM Based Video Frame Selection for Efficient Video Understanding
Kai Hu, Feng Gao, Xiaohan Nie +8
Recent advances in Multi-Modal Large Language Models (M-LLMs) show promising results in video reasoning. Popular Multi-Modal Large Language Model (M-LLM) frameworks usually apply n…
Bringing Multimodality to Amazon Visual Search System
Xinliang Zhu, Michael Huang, Han Ding +10
Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning model matching visual patterns betw…
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models
Shwetha Ram, Tal Neiman, Qianli Feng +3
Given a small number of images of a subject, personalized image generation techniques can fine-tune large pre-trained text-to-image diffusion models to generate images of the subje…
X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs
Sirnam Swetha, Jinyu Yang, Tal Neiman +5
Recent advancements in Multimodal Large Language Models (MLLMs) have revolutionized the field of vision-language understanding by integrating visual perception capabilities into La…