Deep Learning Techniques for Monocular Depth Estimation | Vladimir Kovacevic | DSC Europe 23

In his tech tutorial, Vladimir provided a deep dive into the intricacies of monocular depth estimation, a challenging problem in computer vision due to its inherent mathematical ambiguity. Vladimir guided participants through influential methodologies that have emerged in recent years to tackle this issue. The tutorial commenced by exploring the transformative impact of transfer learning in monocular depth estimation, followed by a practical examination of adaptive binning techniques. Vladimir then delved into the innovative application of Vision Transformers, highlighting their role in addressing the complexities of the problem. Central to the discussion was the unveiling of the groundbreaking DINOv2 model by Meta AI, a cutting-edge development that integrates breakthroughs from Natural Language Processing (NLP) into the field of computer vision. The tutorial offered a comprehensive journey into the evolving landscape of monocular depth estimation, presenting participants with valuable insights into state-of-the-art techniques and models.

This tutorial by Vladimir Kovacevic was held on November 20th as part of Tech Tutorials on Stream 2 live at the Data Science Conference Europe 2023.

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