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Articles by Imad Eddine
PICOGRAM - informing coral reef resilience-based management through prediction of individual coral organismal growth, recruitment, and mortality
Abstract Coral reef science and management are increasingly relying on imagery-based methods to expand monitoring capacity and improve the assessment of reef condition. However, scalable monitoring requires coral colony-scale segmentation from high-resolution underwater imagery and orthomosaic-derived survey products, while dense manual pixel-wise annotation remains costly, time-consuming, and inconsistent across sites and imaging conditions.
Cm-scale marine habitat mapping of entire Tumon Bay, Guam Coral Reef using NASA airborne fluid lensing and NeMO-Net pre (2022) and post (2024) Typhoon Mawar
Abstract Introduction: High-resolution imaging of coral reefs is critical for understanding ecosystem dynamics and guiding conservation efforts yet monitoring these changes at ecologically relevant spatial and temporal scales remains challenging. Methods: We present cm-scale benthic habitat maps for over 5 km2 of coral reefs in Tumon Bay, Guam, for 2022 and 2024, before and after Category 4 Typhoon Mawar (May 2023).
Automated airborne detection of underwater munitions using NASA multispectral passive and active MiDAR Fluid Lensing
Abstract Introduction: We explored using several novel airborne aquatic remote sensing technologies for the automated airborne detection and localization of underwater military munitions in a complex marine environment.
Binary reformulation for marine debris detection in Sentinel-2 imagery: an empirical study on extreme class imbalance using the first benchmarks on combined MARIDA and MADOS datasets
Abstract Introduction: Marine debris detection from satellite imagery is challenged by two major factors: extreme class imbalance, with debris pixels accounting for less than 0.01% of image content, and the need for robust generalization across diverse geographic and temporal domains for operational deployment.
HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback
[Submitted on 25 Jul 2025] Title:HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback View a PDF of the paper titled HQ-SMem: Video Segmentation and Tracking Using Memory Efficient Object Embedding With Selective Update and Self-Supervised Distillation Feedback, by Elham Soltani Kazemi and 4 other authors View PDF HTML (experimental) Abstract:Video Object Segmentation (VOS) is foundational to numerous...
No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only Memory
[Submitted on 6 Feb 2025] Title:No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only Memory View a PDF of the paper titled No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only Memory, by Imad Eddine Marouf and 3 other authors View PDF HTML (experimental) Abstract:Continual Learning in Visual Question Answering (VQACL) requires models to learn new visual-linguistic tasks (plasticity) while retaining knowledge from previous tasks (stability).
[2410.14159] Assessing Open-world Forgetting in Generative Image Model Customization
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Riemannian Geometry-Based EEG Approaches: A Literature Review
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Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use
[Submitted on 5 Mar 2024] Title:Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use Authors:Imad Eddine Toubal , Aditya Avinash , Neil Gordon Alldrin , Jan Dlabal , Wenlei Zhou , Enming Luo , Otilia Stretcu , Hao Xiong , Chun-Ta Lu , Howard Zhou , Ranjay Krishna , Ariel Fuxman , Tom Duerig Download a PDF of the paper titled Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use, by...
[2310.11482] Enhancing Plasticity for First Session Adaptation Continual Learning
[Submitted on 17 Oct 2023 ( v1 ), last revised 8 Jul 2025 (this version, v3)] Title:Enhancing Plasticity for First Session Adaptation Continual Learning View a PDF of the paper titled Enhancing Plasticity for First Session Adaptation Continual Learning, by Imad Eddine Marouf and 3 other authors View PDF HTML (experimental) Abstract:The integration of large pre-trained models (PTMs) into Class-Incremental Learning (CIL) has facilitated the development of computationally efficient strategies...
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