8 citations · 16 across the 15 of their papers we have counts for
19 papers
MoG-VLM: Efficient Video Anomaly Detection through Motion-Guided Vision-Language Models
Khaled Waleed Dawoud, Muhammad Zaigham Zaheer, Khan Muhammad +1
Vision-Language Models (VLMs) have shown strong potential for video anomaly detection, but dense frame-level inference makes them impractical for real-time deployment. Existing acc…
Beyond Similarity: Foundation Models as an Efficient Backbone for Training-Free Composed Video Retrieval
Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar +2
Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gallery scale, this creates a fu…
SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions
Markus Essl, Marta Moscati, Mubashir Noman +4
Multimodal sensor fusion has demonstrated remarkable performance improvements over unimodal approaches in 3D object detection for autonomous vehicles. Typically, existing methods t…
OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
Akashah Shabbir, Muhammad Umer Sheikh, Muhammad Akhtar Munir +8
Recent progress in multimodal reasoning has enabled agents that interpret imagery, connect it with language, and execute structured analytical tasks. Extending these capabilities t…
Face-Voice Association with Inductive Bias for Maximum Class Separation
Marta Moscati, Oleksandr Kats, Mubashir Noman +4
Face-voice association is widely studied in multimodal learning and is approached representing faces and voices with embeddings that are close for a same person and well separated…
Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge
Marta Moscati, Ahmed Abdullah, Muhammad Saad Saeed +7
Over half of the world's population is bilingual and people often communicate under multilingual scenarios. The Face-Voice Association in Multilingual Environments (FAME) 2026 Chal…
RobustA: Robust Anomaly Detection in Multimodal Data
Salem AlMarri, Muhammad Irzam Liaqat, Muhammad Zaigham Zaheer +3
In recent years, multimodal anomaly detection methods have demonstrated remarkable performance improvements over video-only models. However, real-world multimodal data is often cor…
Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan
Marta Moscati, Ahmed Abdullah, Muhammad Saad Saeed +7
The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, audio-visual systems are among the most widely used multimo…
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
Umair Nawaz, Muhammad Zaigham Zaheer, Ufaq Khan +4
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenge…
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood +2
Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain…
Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach
Muhammad Saad Saeed, Shah Nawaz, Muhammad Zaigham Zaheer +6
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that…
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
Asifullah Khan, Anabia Sohail, Mustansar Fiaz +11
Advances in deep learning are re-defining how visual data is processed and understand by the machines. Vision Transformers (ViTs) have recently demonstrated prominent performance i…
Chameleon: Images Are What You Need For Multimodal Learning Robust To Missing Modalities
Muhammad Irzam Liaqat, Shah Nawaz, Muhammad Zaigham Zaheer +5
Multimodal learning has demonstrated remarkable performance improvements over unimodal architectures. However, multimodal learning methods often exhibit deteriorated performances i…
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada +1
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or repr…
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood +1
Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more random…
Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan
Muhammad Saad Saeed, Shah Nawaz, Muhammad Salman Tahir +7
The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, the audio-visual systems are one of the widely used multimo…
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline
Anas Al-lahham, Muhammad Zaigham Zaheer, Nurbek Tastan +1
Unsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity recently due to its practical real-world applications. As surveillance video…
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
In order to devise an anomaly detection model using only normal training data, an autoencoder (AE) is typically trained to reconstruct the data. As a result, the AE can extract nor…
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection
Anas Al-lahham, Nurbek Tastan, Zaigham Zaheer +1
Detection of anomalous events in videos is an important problem in applications such as surveillance. Video anomaly detection (VAD) is well-studied in the one-class classification…