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of 180
pro vyhledávání: '"Yao, Jingfeng"'
Diffusion Transformers (DiT) have attracted significant attention in research. However, they suffer from a slow convergence rate. In this paper, we aim to accelerate DiT training without any architectural modification. We identify the following issue
Externí odkaz:
http://arxiv.org/abs/2410.10356
The segmentation of cell nuclei in tissue images stained with the blood dye hematoxylin and eosin (H$\&$E) is essential for various clinical applications and analyses. Due to the complex characteristics of cellular morphology, a large receptive field
Externí odkaz:
http://arxiv.org/abs/2407.18054
Autor:
Yao, Jingfeng, Wang, Xinggang, Song, Yuehao, Zhao, Huangxuan, Ma, Jun, Chen, Yajie, Liu, Wenyu, Wang, Bo
The diagnosis and treatment of chest diseases play a crucial role in maintaining human health. X-ray examination has become the most common clinical examination means due to its efficiency and cost-effectiveness. Artificial intelligence analysis meth
Externí odkaz:
http://arxiv.org/abs/2405.05237
Gaze following aims to interpret human-scene interactions by predicting the person's focal point of gaze. Prevailing approaches often use multi-modality inputs, most of which adopt a two-stage framework. Hence their performance highly depends on the
Externí odkaz:
http://arxiv.org/abs/2403.12778
The twig edge states in graphene-like structures are viewed as the fourth states complementary to their zigzag, bearded, and armchair counterparts. In this work, we study a rod-in-plasma system in honeycomb lattice with twig edge truncation under ext
Externí odkaz:
http://arxiv.org/abs/2311.08733
Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the trimap guidance. However, the production of trimap often requires significant labor, which limits the widespread application of matting algorithms on a large
Externí odkaz:
http://arxiv.org/abs/2306.04121
Recently, plain vision Transformers (ViTs) have shown impressive performance on various computer vision tasks, thanks to their strong modeling capacity and large-scale pretraining. However, they have not yet conquered the problem of image matting. We
Externí odkaz:
http://arxiv.org/abs/2305.15272
Publikováno v:
In Image and Vision Computing July 2024 147
Publikováno v:
In Information Fusion March 2024 103
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