Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts.

A vision model with general-purpose object-level 3D understanding should be capable of inferring both 2D ( e.g., class name and bounding box) and 3D information ( e.g., 3D location and 3D viewpoint) for arbitrary rigid objects in natural

Visual question answering ( VQA) is a challenging task that requires an in-depth understanding of vision and language, as well as multi-modal reasoning.