52 citations · 89 across the 16 of their papers we have counts for
18 papers
HUMAN-TCI: Hierarchical Multi-Stream Motion-Aware Network with Torso-Centered Interaction for Text-to-Motion Retrieval
Muhammad Islam, Euijoon Ahn, Usman Naseem +1
Accurate retrieval of human motions is a crucial first step in text-guided human motion modeling and synthesis, as it selects semantically relevant sequences from large datasets an…
Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification
Yupeng Zhang, Adam G. Dunn, Usman Naseem +1
Medical artificial intelligence (AI) systems, particularly multimodal vision-language models (VLM), often exhibit intersectional biases where models are systematically less confide…
MRG-R1: Reinforcement Learning for Clinically Aligned Medical Report Generation
Pengyu Wang, Shuchang Ye, Usman Naseem +1
Medical report generation aims to automatically produce radiology-style reports from medical images, supporting efficient and accurate clinical decision-making.However, existing ap…
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
Shuchang Ye, Usman Naseem, Mingyuan Meng +1
Medical language-guided segmentation, integrating textual clinical reports as auxiliary guidance to enhance image segmentation, has demonstrated significant improvements over unimo…
MRGAgents: A Multi-Agent Framework for Improved Medical Report Generation with Med-LVLMs
Pengyu Wang, Shuchang Ye, Usman Naseem +1
Medical Large Vision-Language Models (Med-LVLMs) have been widely adopted for medical report generation. Despite Med-LVLMs producing state-of-the-art performance, they exhibit a bi…
MedCFVQA: A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering
Shuchang Ye, Usman Naseem, Mingyuan Meng +2
Medical Visual Question Answering (MedVQA) is crucial for enhancing the efficiency of clinical diagnosis by providing accurate and timely responses to clinicians' inquiries regardi…
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets
Yongpei Ma, Pengyu Wang, Adam Dunn +2
Medical Visual Question Answering (MVQA) systems can interpret medical images in response to natural language queries. However, linguistic variability in question phrasing often un…
Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation
Hao Wang, Shuchang Ye, Jinghao Lin +2
Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support. Large vision-language models (LVLMs) hold great pr…
Enhancing ESG Impact Type Identification through Early Fusion and Multilingual Models
Hariram Veeramani, Surendrabikram Thapa, Usman Naseem
In the evolving landscape of Environmental, Social, and Corporate Governance (ESG) impact assessment, the ML-ESG-2 shared task proposes identifying ESG impact types. To address thi…
MSynFD: Multi-hop Syntax aware Fake News Detection
Liang Xiao, Qi Zhang, Chongyang Shi +3
The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contex…
Dynamic Traceback Learning for Medical Report Generation
Shuchang Ye, Mingyuan Meng, Mingjian Li +3
Automated medical report generation has demonstrated the potential to significantly reduce the workload associated with time-consuming medical reporting. Recent generative represen…
Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines
Farhan Ahmad Jafri, Mohammad Aman Siddiqui, Surendrabikram Thapa +3
The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages. To address this issue, we introduce a new…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model
Usman Naseem, Byoung Chan Lee, Matloob Khushi +2
A user-generated text on social media enables health workers to keep track of information, identify possible outbreaks, forecast disease trends, monitor emergency cases, and ascert…
Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT
Usman Naseem, Adam G. Dunn, Matloob Khushi +1
The availability of biomedical text data and advances in natural language processing (NLP) have made new applications in biomedical NLP possible. Language models trained or fine tu…
Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU
Usman Naseem, Matloob Khushi, Jinman Kim +1
Vaccines are an important public health measure, but vaccine hesitancy and refusal can create clusters of low vaccine coverage and reduce the effectiveness of vaccination programs.…
Text Mining of Stocktwits Data for Predicting Stock Prices
Mukul Jaggi, Priyanka Mandal, Shreya Narang +2
Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial…
BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition
Usman Naseem, Matloob Khushi, Vinay Reddy +3
In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recogn…