19 citations · 46 across the 30 of their papers we have counts for
12 papers · 1 filter
Cog-VADU: A Training-Free Cognitive Reasoning Framework for Video Anomaly Detection and Understanding
Mohd Ubaid Wani, Sara Atito, Josef Kittler +1
Video Anomaly Detection (VAD) aims to temporally localize abnormal events in videos. Most existing approaches rely on dataset-specific training and curated annotations, limiting ge…
Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images
Umar Marikkar, Sameed Husain, Muhammad Awais +1
Multi-Channel imaging (MCI) data differs fundamentally from natural images, as each channel records a semantically distinct signal rather than a colour band. To adapt vision encode…
From Gaze to Meaning: A Training-Free AI Agent for Unified Grounding and Explanation
Shayan Nasiriboukani, Sara Atito, Mohammad Nezamipour +1
Understanding human attention is fundamental for scene interpretation, yet existing approaches often rely on heavily trained models that lack interpretability. Prior methods strugg…
Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization
Tony Alex, Wish Suharitdamrong, Sara Atito +5
Large Audio Language Models (LALMs) have made rapid progress on standardized benchmarks, yet their deployment in practical media workflows, curation, archival indexing, and content…
MorphUNet: Alpha-Controlled Biometric Transport for Diffusion-Based Face Morphing Attacks
Taimoor Rizwan, Sara Atito, Zhenhua Feng +2
Face morphing attacks create synthetic images verifiable against multiple identities, threatening border control and identity verification systems. We introduce MorphUNet, a diffus…
Diff-ID: Identity Consistent Facial Image Generation and Morphing via Diffusion Models
Taimoor Rizwan, Sara Atito, Muhammad Awais +2
Generative diffusion models have revolutionized facial image synthesis, yet robust identity preservation in high resolution outputs remains a critical challenge. This issue is espe…
From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
Wish Suharitdamrong, Muhammad Awais, Xiatian Zhu +1
Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer? Despite their growing role…
Information theoretic underpinning of self-supervised learning by clustering
Josef Kittler, Sara Atito, Muhammad Awais
Self-supervised learning (SSL) is recognized as an essential tool for building foundation models for Artificial Intelligence applications. The advances in SSL have been made thanks…
See Fair, Speak Truth: Equitable Attention Improves Grounding and Reduces Hallucination in Vision-Language Alignment
Mohammad Anas Azeez, Ankan Deria, Zohaib Hasan Siddiqui +5
Multimodal large language models (MLLMs) frequently hallucinate objects that are absent from the visual input, often because attention during decoding is disproportionately drawn t…
CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks
Wish Suharitdamrong, Tony Alex, Muhammad Awais +1
Foundation models have revolutionized AI, but adapting them efficiently for multimodal tasks, particularly in dual-stream architectures composed of unimodal encoders, such as DINO…
Domain Adaptation Without the Compute Burden for Efficient Whole Slide Image Analysis
Umar Marikkar, Muhammad Awais, Sara Atito
Computational methods on analyzing Whole Slide Images (WSIs) enable early diagnosis and treatments by supporting pathologists in detection and classification of tumors. However, th…
Channel-Aware Probing for Multi-Channel Imaging
Umar Marikkar, Syed Sameed Husain, Muhammad Awais +1
Training and evaluating vision encoders on Multi-Channel Imaging (MCI) data remains challenging as channel configurations vary across datasets, preventing fixed-channel training an…