To tackle this issue, we conducted the theoretical analysis to promote the effectiveness of contrast decoding.

Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor.

In the visual spatial understanding (VSU) area, spatial image-to-text (SI2T) and spatial text-to-image (ST2I) are two fundamental tasks that appear in dual form. Existing methods for standalone SI2T or ST2I perform imperfectly in spatial understanding, due to the difficulty of 3D-wise spatial feature modeling.

The word embedding space in neural models is skewed, and correcting this can improve task performance.

The Rashomon effect is a mixed blessing in responsible machine learning.