MBZUAI researchers presented a new approach to video analysis at ICCV in Paris, led by Syed Talal Wasim. The approach builds on still image processing techniques like focal modulation to analyze spatial and temporal information in video separately. It aims to improve temporal aggregation while avoiding the computational complexity of transformers. Why it matters: This research advances video understanding in computer vision by offering a more efficient method for temporal modeling, crucial for applications like activity recognition and video surveillance.
Thamar Solorio from the University of Houston presented preliminary work on multimodal representation learning for detecting objectionable content in videos at MBZUAI. The research investigates two multimodal pretraining mechanisms, finding contrastive learning more effective than unimodal representation prediction. The study also assesses the value of common multimodal corpora for this task. Why it matters: This research contributes to the development of AI techniques for content moderation, an important issue for online platforms in the Middle East and globally.
Salem AlMarri, the first Emirati Ph.D. graduate from MBZUAI, developed a video anomaly detection (VAD) system for his thesis. The VAD system can detect subtle anomalies in video, such as suspicious interactions, to help police prevent crimes and save lives. AlMarri's work was carried out under the guidance of Karthik Nandakumar, Affiliated Associate Professor of Computer Vision at MBZUAI. Why it matters: This research showcases the potential of AI in enhancing public safety and security in the UAE, demonstrating practical applications of computer vision in law enforcement.