6 citations · 6 across the 17 of their papers we have counts for
19 papers
The Planning Limits of Latent World Models
Ali Alrasheed, Basim Azam, Naveed Akhtar
World models offer a promising way to help robots understand how the physical world evolves and plan complex behaviours through imagination. Yet existing studies mainly demonstrate…
MEND: Label-Free Detection, Localisation, and Correction of Latent Hallucination in World Models
Ali J Alrasheed, Aryan Yazdan Parast, Basim Azam +2
World Models are appearing as the next major frontier in computer vision. However, their robustness is currently largely unexplored. We identify the phenomenon of hallucination in…
Introspective Attention Modulation for Safe Text-to-Image Generation
Basim Azam, Hossein Rahmani, Naveed Akhtar
State-of-the-art flow based text-to-image (T2I) models exhibit remarkable generative abilities but remain vulnerable to producing unsafe content. Prior safety efforts range from co…
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…
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…
Latent Video Prediction for World Modeling: An Evaluation Uncovering Intriguing Favorable Evidence
Ali J Alrasheed, Aryan Yazdan Parast, Basim Azam +2
Self-supervised video models are increasingly framed as world models, yet they are still evaluated almost entirely on clean video and reported as a final task score, obscuring how…
Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
Sarmad Khan, Basim Azam, Arslan Shaukat
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, Basim Azam, Arslan Shaukat
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…
HSFM: Hard-Set-Guided Feature-Space Meta-Learning for Robust Classification under Spurious Correlations
Aryan Yazdan Parast, Khawar Islam, Soyoun Won +2
Deep neural networks often rely on spurious features to make predictions, which makes them brittle under distribution shift and on samples where the spurious correlation does not h…
Altered Thoughts, Altered Actions: Reasoning Chain as Control Surface for a Vision-Language-Action Policy
Tuan Duong Trinh, Naveed Akhtar, Basim Azam +2
Vision-language-action policies map camera images and natural-language instructions to a robot's motor actions. Some of these policies are designed to reason in text before acting,…
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…
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…
GO-N3RDet: Geometry Optimized NeRF-enhanced 3D Object Detector
Zechuan Li, Hongshan Yu, Yihao Ding +3
We propose GO-N3RDet, a scene-geometry optimized multi-view 3D object detector enhanced by neural radiance fields. The key to accurate 3D object detection is in effective voxel rep…
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…
Suitability of KANs for Computer Vision: A preliminary investigation
Basim Azam, Naveed Akhtar
Kolmogorov-Arnold Networks (KANs) introduce a paradigm of neural modeling that implements learnable functions on the edges of the networks, diverging from the traditional node-cent…
Context-based Deep Learning Architecture with Optimal Integration Layer for Image Parsing
Ranju Mandal, Basim Azam, Brijesh Verma
Deep learning models have been efficient lately on image parsing tasks. However, deep learning models are not fully capable of exploiting visual and contextual information simultan…
Deep Learning Model with GA based Feature Selection and Context Integration
Ranju Mandal, Basim Azam, Brijesh Verma +1
Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Many top-performing methods for image segmentation are bas…
Multipath CNN with alpha matte inference for knee tissue segmentation from MRI
Sheheryar Khan, Basim Azam, Yongcheng Yao +1
Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), which are state…