From the 1 of 13 linked papers with an AI index.
13 papers
Reasoning as a Double-Edged Sword: Architecture and Cross-Stage Robustness in Vision-Language-Action Models
Tuan Duong Trinh, Naveed Akhtar, Basim Azam
Does adding a reasoning step make a Vision-Language-Action (VLA) model more robust to perturbation? Intuitively, a policy that reasons before acting should absorb a perturbed input…
Introspective Attention Modulation for Safe Text-to-Image Generation
Basim Azam, Hossein Rahmani, Naveed Akhtar
The paper proposes a method that monitors and adjusts the attention mechanisms of text‑to‑image diffusion models at inference time to prevent the generation of unsafe content while…
Intermediate Text Representation Guided Text-to-Image Generation for Enhancing One-and-Only Alignment
Soyoun Won, Aryan Yazdan Parast, Basim Azam +2
Text-to-image (T2I) diffusion models often fail to faithfully render explicit textual descriptions, instead defaulting to strongly learned visual priors due to a phenomenon referre…
Invoice Haystack: Benchmarking Document Retrieval and Visual Question Answering Under Strong Visual Homogeneity
Heethanjan Kanagalingam, Thenukan Pathmanathan, Mokeeshan Vathanakumar +3
Vision Language Models have achieved near-human performance on single-document Visual Question Answering, yet their effectiveness degrades significantly when retrieving information…
Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
Sarmad Khan, Arslan Shaukat, Umer Asgher +1
In recent years, there has been a notable increase in the level of attention that is given to algorithms based on deep learning in the context of medical image segmentation. Nevert…
A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
Sarmad Khan, Arslan Shaukat, Umer Asgher +1
COVID-19 was a significant challenge that led to the loss of numerous lives daily. Not only a certain country was involved in this outbreak, but even the world has suffered because…