VGG Transformer Framework Explained

This chapter explores the VGG Transformer framework, which is capable of handling a variable number of input frames for visual tasks. The discussion includes concepts such as patchifying input frames, a learnable camera token for parameter estimation, and the introduction of Alternate Attention for scene reconstruction. Additionally, the speakers emphasize the role of temporal continuity and balanced loss weighting in enhancing model performance across tasks like localization and mapping.