2 citations · 3 across the 9 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…
MLIM: Vision-and-Language Model Pre-training with Masked Language and Image Modeling
Tarik Arici, Mehmet Saygin Seyfioglu, Tal Neiman +5
Vision-and-Language Pre-training (VLP) improves model performance for downstream tasks that require image and text inputs. Current VLP approaches differ on (i) model architecture (…
Hypergraph Pre-training with Graph Neural Networks
Boxin Du, Changhe Yuan, Robert Barton +2
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus…
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution
Robert A. Barton, Tal Neiman, Changhe Yuan
Online stores often utilize product relationships such as bundles and substitutes to improve their catalog quality and guide customers through myriad choices. Entity resolution usi…