1 citations · 1 across the 7 of their papers we have counts for
7 papers
Brick-DICL: Dynamic In-Context Learning for Automated Brick Schema Classification
Yiyue Qian, Shinan Zhang, Huan Song +3
Building Management Systems (BMS) are essential for optimizing energy efficiency and operational performance in modern buildings. However, the lack of standardization across BMS po…
From Prompt to Production:Automating Brand-Safe Marketing Imagery with Text-to-Image Models
Parmida Atighehchian, Henry Wang, Andrei Kapustin +4
Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying…
Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs
Dongkyu Derek Cho, Huan Song, Arijit Ghosh Chowdhury +6
Fine-tuning large language models (LLMs) for downstream tasks typically exhibit a fundamental safety-capability tradeoff, where improving task performance degrades safety alignment…
Improved Few-Shot Image Classification Through Multiple-Choice Questions
Dipika Khullar, Emmett Goodman, Negin Sokhandan
Through a simple multiple choice language prompt a VQA model can operate as a zero-shot image classifier, producing a classification label. Compared to typical image encoders, VQA…
Automated Virtual Product Placement and Assessment in Images using Diffusion Models
Mohammad Mahmudul Alam, Negin Sokhandan, Emmett Goodman
In Virtual Product Placement (VPP) applications, the discrete integration of specific brand products into images or videos has emerged as a challenging yet important task. This pap…
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts
Wenzhen Zhu, Negin Sokhandan, Guang Yang +2
Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc. While digitization of documents such as scienti…
A Few-Shot Sequential Approach for Object Counting
Negin Sokhandan, Pegah Kamousi, Alejandro Posada +2
In this work, we address the problem of few-shot multi-class object counting with point-level annotations. The proposed technique leverages a class agnostic attention mechanism tha…