activity
20242026
most citedHumanity's Last Exam

18 citations · 37 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios

Imad Ali Shah, Jiarong Li, Tim Brophy +4

Recent advances in autonomous driving (AD) have highlighted the potential of hyperspectral imaging (HSI) for enhanced environmental perception, particularly in challenging weather…

cs.CV20261 cited

Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation

Imad Ali Shah, Jiarong Li, Ethan Delaney +4

Hyperspectral sensing provides rich spectral information for scene understanding in urban driving, but its high dimensionality poses challenges for interpretation and efficient lea…

cs.CV2026

Vision-Language Models vs Human: Perceptual Image Quality Assessment

Imran Mehmood, Imad Ali Shah, Ming Ronnier Luo +1

Psychophysical experiments remain the most reliable approach for perceptual image quality assessment (IQA), yet their cost and limited scalability encourage automated approaches. W…

cs.LG202618 cited

Humanity's Last Exam

Long Phan, Alice Gatti, Ziwen Han +1144

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…

cs.CV2025

Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities

Imad Ali Shah, Jiarong Li, Roshan George +5

Hyperspectral imaging (HSI) offers a transformative sensing modality for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD) applications, enabling material-level…

cs.CV2025

Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study

Jiarong Li, Imad Ali Shah, Enda Ward +3

Pedestrian segmentation in automotive perception systems faces critical safety challenges due to metamerism in RGB imaging, where pedestrians and backgrounds appear visually indist…