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Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor.
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image.
Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Image Y u Zhao
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.
Zipfian Whitening
The word embedding space in neural models is skewed, and correcting this can improve task performance.
EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models
Our experiments with extensive data indicate that the model trained on generated data of the advanced model can approximate its generation capability. However, it requires large-scale samples of 10 million or more.