ANC Workshop - Mehmet Aygun

Tuesday, 19th March 2024

Enhancing 2D Representation Learning with a 3D Prior - Mehmet Aygun

Abstract: Learning robust representations of visual data is crucial in computer vision. Traditionally, this requires expensive labeled data. Self-supervised learning aims to overcome this by learning from raw visual data alone. However, most current methods focus on 2D images, lacking rich 3D information like humans. We propose enhancing self-supervised methods by integrating a strong 3D structural prior during training. Our experiments across various datasets show that our approach yields more robust representations compared to conventional self-supervised methods.

Event type: Workshop

Date: Tuesday, 19th March

Time: 11:00

Location: G.03

Speaker(s): Mehmet Aygun

Chair/Host: Bryan Li