ChatGPT is all you need to decolonize sub-Saharan Vocational Education

The advances of Generative AI models with interactive capabilities over the past few years offer unique opportunities for socioeconomic mobility. Their potential for scalability, accessibility, affordability, personalizing and convenience sets a first-class opportunity for poverty-stricken countries to adapt and modernize their educational order. As a result, this position paper makes the case for an educational policy framework that would succeed in this transformation by prioritizing vocational and technical training over academic education in sub-Saharan African countries. We highlight substantial applications of Large Language Models, tailor-made to their respective cultural background(s) and needs, that would reinforce their systemic decolonization. Lastly, we provide specific historical examples of diverse states successfully implementing such policies in the elementary steps of their socioeconomic transformation, in order to corroborate our proposal to sub-Saharan African countries to follow their lead.

How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking

We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects. Such a problem poses multiple challenges originating both from the objects' nature and their interaction with their environment, which previous approaches have failed to fully address. The proposed framework encapsulates methods for background clutter and occlusion handling by integrating multiple parallel soft spatial attention modules into a multitask Convolutional Neural Network (CNN) architecture. Moreover, we consider the special geometrical properties of both the object's 3D model and the pose space, and we use a more sophisticated approach for data augmentation for training. The provided experimental results confirm the effectiveness of the proposed multi-attentional architecture, as it improves the State-of-the-Art (SoA) tracking performance by an average score of 40.5% for translation and 57.5% for rotation, when testing on the dataset presented in [1], the most complete dataset designed, up to date, for the problem of RGB-D object tracking.