Muhammad Zaigham Zaheer — accepted AI-conference papers

2026

Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge

Marta Moscati, Ahmed Abdullah, Muhammad Saad Saeed, Shah Nawaz, Rohan Kumar Das, Muhammad Zaigham Zaheer +3

ICASSP 2026poster

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 Challenge, held at ICASSP 2026, focuses on developing methods for face-voice association that are effective when the languag…

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2026

Thinking Beyond Labels: Vocabulary-Free Fine-Grained Recognition using Reasoning-Augmented LMMs

Dmitry Demidov, Muhammad Zaigham Zaheer, Zongyan Han, Omkar Thawakar, Rao Anwer

CVPR 2026

Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Existing solutions for this problem remain limited by either the usage of a large and rigid list of vocabularies or by the dependency on comple

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2024

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline

Anas Al-lahham, Muhammad Zaigham Zaheer, Nurbek Tastan, Karthik Nandakumar

CVPR 2024poster

nsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity lately due to its practical real-world applications. Due to the extremely challenging nature of this task where learning is carried out without any annotations privacy-critical collaborative learni…

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2024

DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models

Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar

CVPR 2024poster

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 randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important por…

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2023

Single-branch Network for Multimodal Training

Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan, Muhammad Zaigham Zaheer, Karthik Nandakumar, Muhammad Haroon Yousaf +1

ICASSP 2023accepted

With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text. Researchers have focused on building autonomous systems capable of processing such multimedia data to solve challenging multimodal tasks including cross-modal retrieval…

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2020

CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection

Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee

ECCV 2020poster

Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including 1) a random batch b…

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2020

Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm

Muhammad Zaigham Zaheer, Jin-Ha Lee, Marcella Astrid, Seung-Ik Lee

CVPR 2020poster

A popular method for anomaly detection is to use the generator of an adversarial network to formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of anomalies, optimizing such networks can be a cumbersome task. Another possible approach is to use both generator and di…

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