30 citations · 31 across the 6 of their papers we have counts for
6 papers
MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning
Chengfei Wu, Ronald Seoh, Bingxuan Li +3
Recent advances in large vision-language models have led to impressive performance in visual question answering and multimodal reasoning. However, it remains unclear whether these…
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding
Ronald Seoh, Dan Goldwasser
In this paper, we introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. The key intuition of our approach is that…
Encoding Multi-Domain Scientific Papers by Ensembling Multiple CLS Tokens
Ronald Seoh, Haw-Shiuan Chang, Andrew McCallum
Many useful tasks on scientific documents, such as topic classification and citation prediction, involve corpora that span multiple scientific domains. Typically, such tasks are ac…
Open Aspect Target Sentiment Classification with Natural Language Prompts
Ronald Seoh, Ian Birle, Mrinal Tak +3
For many business applications, we often seek to analyze sentiments associated with any arbitrary aspects of commercial products, despite having a very limited amount of labels or…
Solving Bayesian Network Structure Learning Problem with Integer Linear Programming
Ronald Seoh
This dissertation investigates integer linear programming (ILP) formulation of Bayesian Network structure learning problem. We review the definition and key properties of Bayesian…
Qualitative Analysis of Monte Carlo Dropout
Ronald Seoh
In this report, we present qualitative analysis of Monte Carlo (MC) dropout method for measuring model uncertainty in neural network (NN) models. We first consider the sources of u…