Skip to content

Multimodal Classifier For Space Target Recognition

In this paper, we propose a multi-modal framework to tackle the SPARK Challenge by classifying satellites using RGB and depth images. Our framework is mainly based on Auto-Encoders (AE)s to embed the two modalities in a common latent space in order to exploit redundant and complementary information between the two types of data.

Ichraf Lahouli, Mahmoud Jarraya, and Gianmarco Aversano, Multimodal Classifier For Space Target Recognition, In Proc. of The 2021 IEEE International Conference on Image Processing, September 2021.

Click here to access the paper.

Releated Posts

Muppet: A Modular and Constructive Decomposition for Perturbation-based Explanation Methods

The topic of explainable AI has recently received attention driven by a growing awareness of the need for transparent and accountable AI. In this paper, we propose a novel methodology to decompose any state-of-the-art perturbation-based explainability approach into four blocks. In addition, we provide Muppet: an open-source Python library for explainable AI.
Read More

Insights from GTC Paris 2025

Among the NVIDIA GTC Paris crowd was our CTO Sabri Skhiri, and from quantum computing breakthroughs to the full-stack AI advancements powering industrial digital twins and robotics, there is a lot to share! Explore with Sabri GTC 2025 trends, keynotes, and what it means for businesses looking to innovate.
Read More