168 citations · 295 across the 17 of their papers we have counts for
18 papers
Benchmarking Multimodal Large Language Models for Zero-Shot Human-Object Interaction
Oriol Rabasseda, Zenjie Li, Kamal Nasrollahi +1
Multimodal Large Language Models (MLLMs) have demonstrated strong zero-shot reasoning across diverse domains, but their potential for Human-Object Interaction (HOI) remains unexplo…
MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams
Iñaki Erregue, Kamal Nasrollahi, Sergio Escalera
Deploying modern video trackers at scale is bottlenecked by the computational cost of RGB-based object detectors. To this end, we present MVTrack, an ultrafast tracker for moving o…
CIFA: Contextual-Intersectional Fairness Auditing for Hidden Subgroup Discovery in Face Analysis
Nazia Aslam, Khalid Adnan Alsayed, Thomas B. Moeslund +1
Fairness evaluation in computer vision commonly relies on aggregate accuracy and demographic subgroup analysis. However, visual models are also sensitive to contextual factors such…
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6
Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on norm…
A Hyperbolic Perspective on Hierarchical Structure in Object-Centric Scene Representations
Neelu Madan, Àlex Pujol, Andreas Møgelmose +4
Slot attention has emerged as a powerful framework for unsupervised object-centric learning, decomposing visual scenes into a small set of compact vector representations called \em…
Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection
Nazia Aslam, Abhisek Ray, Thomas B. Moeslund +1
Video anomaly detection (VAD) systems are increasingly deployed in safety critical environments and require a large amount of data for accurate detection. However, such data may co…
From Pixels to Privacy: Temporally Consistent Video Anonymization via Token Pruning for Privacy Preserving Action Recognition
Nazia Aslam, Abhisek Ray, Joakim Bruslund Haurum +2
Recent advances in large-scale video models have significantly improved video understanding across domains such as surveillance, healthcare, and entertainment. However, these model…
SOVABench: A Vehicle Surveillance Action Retrieval Benchmark for Multimodal Large Language Models
Oriol Rabasseda, Zenjie Li, Kamal Nasrollahi +1
Automatic identification of events and recurrent behavior analysis are critical for video surveillance. However, most existing content-based video retrieval benchmarks focus on sce…
PrismVAU: Prompt-Refined Inference System for Multimodal Video Anomaly Understanding
Iñaki Erregue, Kamal Nasrollahi, Sergio Escalera
Video Anomaly Understanding (VAU) extends traditional Video Anomaly Detection (VAD) by not only localizing anomalies but also describing and reasoning about their context. Existing…
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization
Nazia Aslam, Kamal Nasrollahi
The rapid development of video surveillance systems for object detection, tracking, activity recognition, and anomaly detection has revolutionized our day-to-day lives while settin…
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU
Àlex Pujol Vidal, Sergio Escalera, Kamal Nasrollahi +1
Machine unlearning methods have become increasingly important for selective concept removal in large pre-trained models. While recent work has explored unlearning in Euclidean cont…
YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
Iñaki Erregue, Kamal Nasrollahi, Sergio Escalera
We introduce YOLO11-JDE, a fast and accurate multi-object tracking (MOT) solution that combines real-time object detection with self-supervised Re-Identification (Re-ID). By incorp…
Verifying Machine Unlearning with Explainable AI
Àlex Pujol Vidal, Anders S. Johansen, Mohammad N. S. Jahromi +3
We investigate the effectiveness of Explainable AI (XAI) in verifying Machine Unlearning (MU) within the context of harbor front monitoring, focusing on data privacy and regulatory…
Visual Context-Aware Person Fall Detection
Aleksander Nagaj, Zenjie Li, Dim P. Papadopoulos +1
As the global population ages, the number of fall-related incidents is on the rise. Effective fall detection systems, specifically in healthcare sector, are crucial to mitigate the…
CL-MAE: Curriculum-Learned Masked Autoencoders
Neelu Madan, Nicolae-Catalin Ristea, Kamal Nasrollahi +2
Masked image modeling has been demonstrated as a powerful pretext task for generating robust representations that can be effectively generalized across multiple downstream tasks. T…
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
Neelu Madan, Nicolae-Catalin Ristea, Radu Tudor Ionescu +4
Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on indu…
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection
Antonio Barbalau, Radu Tudor Ionescu, Mariana-Iuliana Georgescu +6
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature. Due to its highly accurate results, the method attracted…
Video Transformers: A Survey
Javier Selva, Anders S. Johansen, Sergio Escalera +3
Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadrat…