8 citations · 9 across the 17 of their papers we have counts for
7 papers · 2 filters
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…
SoccerNet 2026 Challenges Results
Anthony Cioppa, Silvio Giancola, Håkan Ardö +102
The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video underst…
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…
AdaSpot: Spend Resolution Where It Matters for Precise Event Spotting
Artur Xarles, Sergio Escalera, Thomas B. Moeslund +1
Precise Event Spotting aims to localize fast-paced actions or events in videos with high temporal precision, a key task for applications in sports analytics, robotics, and autonomo…
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…