13 papers
Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints
Imtiaz Ul Hassan, Öykü Akbulut, Onur Kaya +4
Most Turkish-capable large language models (LLMs) are evaluated using general-purpose benchmarks rather than long, structurally complex domain documents. This paper evaluates five…
Fine-Grained Action Recognition with Cross-Attentive Latent Sparse Experts
Imtiaz Ul Hassan, Tasweer Ahmad, Nik Bessis +1
Fine-grained human action recognition (FHAR) must distinguish visually similar actions that differ mainly in body configuration, timing, or local appearance. RGB representations re…
Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving
Aditya Humnabadkar, Huaizhong Zhang, Ardhendu Behera
Natural-language-based scenario generation offers an intuitive means of describing rare and complex driving interactions, yet it is still uncertain whether training with language-s…
Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification
Muhammad Azeem, Tanveer Hussain, Amr Ahmed +1
Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variability, and subtle visual difference…
ATRACT: A Trustworthy Robotic Autonomous system to support Casualty Triage
Tasweer Ahmad, Rafael Pina, Sandip Pradhan +6
At a time when drones are increasingly associated with hostile operations, we re-purpose them for humanitarian and life-saving applications. However, adapting search and rescue dro…
Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning
Saqib Nazir, Ardhendu Behera
Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directly from bright-field morphology…
TAG-Head: Time-Aligned Graph Head for Plug-and-Play Fine-grained Action Recognition
Imtiaz Ul Hassan, Nik Bessis, Ardhendu Behera
Fine-grained human action recognition (FHAR) is challenging because visually similar actions differ by subtle spatio-temporal cues. Many recent systems enhance discriminability wit…
HistDiT: A Structure-Aware Latent Conditional Diffusion Model for High-Fidelity Virtual Staining in Histopathology
Aasim Bin Saleem, Amr Ahmed, Ardhendu Behera +5
Immunohistochemistry (IHC) is essential for assessing specific immune biomarkers like Human Epidermal growth-factor Receptor 2 (HER2) in breast cancer. However, the traditional pro…
From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving
A. Humnabadkar, A. Sikdar, B. Cave +3
Autonomous driving technologies have achieved significant advances in recent years, yet their real-world deployment remains constrained by data scarcity, safety requirements, and t…
Training-Only Heterogeneous Image-Patch-Text Graph Supervision for Advancing Few-Shot Learning Adapters
Mohammed Rahman Sherif Khan Mohammad, Ardhendu Behera, Sandip Pradhan +2
Recent adapter-based CLIP tuning (e.g., Tip-Adapter) is a strong few-shot learner, achieving efficiency by caching support features for fast prototype matching. However, these meth…
Advancing Cache-Based Few-Shot Classification via Patch-Driven Relational Gated Graph Attention
Tasweer Ahmad, Arindam Sikdar, Sandip Pradhan +1
Few-shot image classification remains difficult under limited supervision and visual domain shift. Recent cache-based adaptation approaches (e.g., Tip-Adapter) address this challen…
Deep Multi-modal Breast Cancer Detection Network
Noor Ul Huda Shah, Tanveer Hussain, Amr Ahmed +3
Automated breast cancer detection via computer vision techniques is challenging due to the complex nature of breast tissue, the subtle appearance of cancerous lesions, and variatio…
High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics
Aditya Humnabadkar, Arindam Sikdar, Benjamin Cave +3
We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving context…