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From the 1 of 6 linked papers with an AI index.

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20242026
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6 papers

cs.CV2026

SISA-Rec: A Semantically Integrated Sequential Recommender with Contrastive Alignment

Soohan Abbasi, Shahid Munir Shah, Rafia Shaikh +1

The paper introduces SISA-Rec, a transformer-based sequential recommender that combines item ID embeddings with BERT-derived text embeddings via gated fusion and contrastive alignm…

cs.CV2025

AttentionDrop: A Novel Regularization Method for Transformer Models

Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Abdul Akbar Khan +2

Transformer-based architectures achieve state-of-the-art performance across a wide range of tasks in natural language processing, computer vision, and speech processing. However, t…

cs.CV2025

TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision

Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Osama Ahmed Khan +3

The modern text-to-image diffusion models boom has opened a new era in digital content production as it has proven the previously unseen ability to produce photorealistic and styli…

cs.CV2024

AI-based Wearable Vision Assistance System for the Visually Impaired: Integrating Real-Time Object Recognition and Contextual Understanding Using Large Vision-Language Models

Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Shahid Munir Shah +3

Visual impairment affects the ability of people to live a life like normal people. Such people face challenges in performing activities of daily living, such as reading, writing, t…

eess.IV2024

A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning

Sumera Rounaq, Shahid Munir Shah, Mahmoud Aljawarneh

COVID-19 is extremely contagious and its rapid growth has drawn attention towards its early diagnosis. Early diagnosis of COVID-19 enables healthcare professionals and government a…

cs.CV2024

Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models

Shahid Munir Shah, Syeda Anshrah Gillani, Mirza Samad Ahmed Baig +2

This study investigates the use of Large Language Models (LLMs) for improved depression detection from users social media data. Through the use of fine-tuned GPT 3.5 Turbo 1106 and…