6 citations · 8 across the 16 of their papers we have counts for
8 papers · 1 filter
Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models
Licheng Zhang, Bach Le, Naveed Akhtar +1
Building compliance checking (BCC) is a critical process for ensuring that constructed facilities meet regulatory standards. A core component of BCC is the accurate enumeration of…
GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs
Aryan Yazdan Parast, Parsa Hosseini, Hesam Asadollahzadeh +4
Object hallucination in Multimodal Large Language Models (MLLMs) is a persistent failure mode that causes the model to perceive objects absent in the image. This weakness of MLLMs…
Assessing the Feasibility of Lightweight Whisper Models for Low-Resource Urdu Transcription
Abdul Rehman Antall, Naveed Akhtar
This study evaluates the feasibility of lightweight Whisper models (Tiny, Base, Small) for Urdu speech recognition in low-resource settings. Despite Urdu being the 10th most spoken…
DoorDet: Semi-Automated Multi-Class Door Detection Dataset via Object Detection and Large Language Models
Licheng Zhang, Bach Le, Naveed Akhtar +1
Accurate detection and classification of diverse door types in floor plans drawings is critical for multiple applications, such as building compliance checking, and indoor scene un…
Large Language Models for Computer-Aided Design: A Survey
Licheng Zhang, Bach Le, Naveed Akhtar +2
Large Language Models (LLMs) have seen rapid advancements in recent years, with models like ChatGPT and DeepSeek, showcasing their remarkable capabilities across diverse domains. W…
Plug-and-Play Interpretable Responsible Text-to-Image Generation via Dual-Space Multi-facet Concept Control
Basim Azam, Naveed Akhtar
Ethical issues around text-to-image (T2I) models demand a comprehensive control over the generative content. Existing techniques addressing these issues for responsible T2I models…
Context-guided Responsible Data Augmentation with Diffusion Models
Khawar Islam, Naveed Akhtar
Generative diffusion models offer a natural choice for data augmentation when training complex vision models. However, ensuring reliability of their generative content as augmentat…
DDB: Diffusion Driven Balancing to Address Spurious Correlations
Aryan Yazdan Parast, Basim Azam, Naveed Akhtar
Deep neural networks trained with Empirical Risk Minimization (ERM) perform well when both training and test data come from the same domain, but they often fail to generalize to ou…