Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Du, Xiangcheng"'
Deep networks have shown impressive performance in the image restoration tasks, such as image colorization. However, we find that previous approaches rely on the digital representation from single color model with a specific mapping function, a.k.a.,
Externí odkaz:
http://arxiv.org/abs/2408.04172
For image inpainting, the existing Denoising Diffusion Probabilistic Model (DDPM) based method i.e. RePaint can produce high-quality images for any inpainting form. It utilizes a pre-trained DDPM as a prior and generates inpainting results by conditi
Externí odkaz:
http://arxiv.org/abs/2407.05875
When dealing with the task of fine-grained scene image classification, most previous works lay much emphasis on global visual features when doing multi-modal feature fusion. In other words, models are deliberately designed based on prior intuitions a
Externí odkaz:
http://arxiv.org/abs/2407.02769
Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios.
Externí odkaz:
http://arxiv.org/abs/2403.13330
Transformer is beneficial for image denoising tasks since it can model long-range dependencies to overcome the limitations presented by inductive convolutional biases. However, directly applying the transformer structure to remove noise is challengin
Externí odkaz:
http://arxiv.org/abs/2304.06346
This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism. S
Externí odkaz:
http://arxiv.org/abs/2211.13984
Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data. Although data synthetic engines can provide vast amounts of annotated training samples, ther
Externí odkaz:
http://arxiv.org/abs/2207.11469
Autor:
Wu, Xingjiao, Xiao, Luwei, Du, Xiangcheng, Zheng, Yingbin, Li, Xin, Ma, Tianlong, Jin, Cheng, He, Liang
The document layout analysis (DLA) aims to decompose document images into high-level semantic areas (i.e., figures, tables, texts, and background). Creating a DLA framework with strong generalization capabilities is a challenge due to document object
Externí odkaz:
http://arxiv.org/abs/2201.09407
Document layout analysis (DLA) plays an important role in information extraction and document understanding. At present, document layout analysis has reached a milestone achievement, however, document layout analysis of non-Manhattan is still a chall
Externí odkaz:
http://arxiv.org/abs/2111.13809
Autor:
Wu, Xingjiao, Xiao, Luwei, Du, Xiangcheng, Zheng, Yingbin, Li, Xin, Ma, Tianlong, Jin, Cheng, He, Liang
Publikováno v:
In Expert Systems With Applications 1 July 2024 245