6 accepted papers
"Pre-training on large-scale datasets has become a fundamental method for training deep neural networks. Pre-training provides a better set of parameters than random initialization, which reduces the training cost of deep neural networks on the target task. In addition, pre-training also provides a…
"Modality alignment serves as the cornerstone for large multi-modal models (LMMs). However, the impact of different attributes (e.g., data type, quality, and scale) of training data on facilitating effective alignment is still under-explored. In this paper, we delve into the influence of training da…
"In recent years, deep generative models have developed rapidly and can generate high-quality images based on input texts. Assessing the quality of synthetic images in a way consistent with human preferences is critical for both generative model evaluation and preferred image selection. Previous wor…
"This paper introduces the Stream Query Denoising (SQD) strategy, a novel and general approach for high-definition map (HD-map) construction. SQD is designed to improve the modeling capability of map elements by learning temporal consistency. Specifically, SQD involves the process of denoising the q…
"Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, the massive computational burden imposed by the retraining of diffusion models, coupled with the extensive sampling steps…
"Denoising diffusion models have emerged as a dominant approach for image generation, however they still suffer from slow convergence in training and color shift issues in sampling. In this paper, we identify that these obstacles can be largely attributed to bias and suboptimality inherent in the de…