19 citations · 46 across the 30 of their papers we have counts for
34 papers
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…
Multi-Disease Deep Learning Framework for GWAS: Beyond Feature Selection Constraints
Iqra Farooq, Sara Atito, Ayse Demirkan +2
Traditional GWAS has advanced our understanding of complex diseases but often misses nonlinear genetic interactions. Deep learning offers new opportunities to capture complex genom…
Dual-Stage Value-Guided Inference with Margin-Based Reward Adjustment for Fast and Faithful VLM Captioning
Ankan Deria, Adinath Madhavrao Dukre, Feilong Tang +5
Despite significant advances in inference-time search for vision-language models (VLMs), existing approaches remain both computationally expensive and prone to unpenalized, low-con…
SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes
Tony Alex, Sara Ahmed, Armin Mustafa +2
Self-supervised pre-trained audio networks have seen widespread adoption in real-world systems, particularly in multi-modal large language models. These networks are often employed…
PAL: Probing Audio Encoders via LLMs -- Audio Information Transfer into LLMs
Tony Alex, Wish Suharitdamrong, Sara Atito +4
Integration of audio perception into large language models (LLMs) is an emerging research area for enabling machine listening applications, yet efficient transfer of rich audio sem…
DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification
Youssef Mohamed, Noran Mohamed, Khaled Abouhashad +5
While Multi-Task Learning (MTL) offers inherent advantages in complex domains such as medical imaging by enabling shared representation learning, effectively balancing task contrib…
One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion
Chunyang Cheng, Tianyang Xu, Zhenhua Feng +7
Advanced image fusion methods mostly prioritise high-level missions, where task interaction struggles with semantic gaps, requiring complex bridging mechanisms. In contrast, we pro…
Probabilistically Aligned View-unaligned Clustering with Adaptive Template Selection
Wenhua Dong, Xiao-Jun Wu, Zhenhua Feng +3
In most existing multi-view modeling scenarios, cross-view correspondence (CVC) between instances of the same target from different views, like paired image-text data, is a crucial…
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition
Rongchang Li, Zhenhua Feng, Tianyang Xu +5
Compositional actions consist of dynamic (verbs) and static (objects) concepts. Humans can easily recognize unseen compositions using the learned concepts. For machines, solving su…
Investigating Self-Supervised Methods for Label-Efficient Learning
Srinivasa Rao Nandam, Sara Atito, Zhenhua Feng +2
Vision transformers combined with self-supervised learning have enabled the development of models which scale across large datasets for several downstream tasks like classification…
Pseudo Labelling for Enhanced Masked Autoencoders
Srinivasa Rao Nandam, Sara Atito, Zhenhua Feng +2
Masked Image Modeling (MIM)-based models, such as SdAE, CAE, GreenMIM, and MixAE, have explored different strategies to enhance the performance of Masked Autoencoders (MAE) by modi…
DailyMAE: Towards Pretraining Masked Autoencoders in One Day
Jiantao Wu, Shentong Mo, Sara Atito +3
Recently, masked image modeling (MIM), an important self-supervised learning (SSL) method, has drawn attention for its effectiveness in learning data representation from unlabeled…
DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
Abhijeet Parida, Daniel Capellan-Martin, Sara Atito +4
Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automa…