665 citations · 2.1k across the 57 of their papers we have counts for
9 papers · 1 filter
Exploring Vision Transformers as Diffusion Learners
He Cao, Jianan Wang, Tianhe Ren +4
Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone whic…
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning
Nan Xue, Tianfu Wu, Song Bai +4
This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAW…
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer
Jiaqi Ma, Shengyuan Yan, Lefei Zhang +2
In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for…
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
Di Wang, Qiming Zhang, Yufei Xu +4
Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalabili…
HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection
Meiqi Hu, Chen Wu, Liangpei Zhang
The fast development of self-supervised learning lowers the bar learning feature representation from massive unlabeled data and has triggered a series of research on change detecti…
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li, Kai Zhang, Radu Timofte +108
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…
Winograd Convolution: A Perspective from Fault Tolerance
Xinghua Xue, Haitong Huang, Cheng Liu +3
Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than…
Fully Convolutional Change Detection Framework with Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection
Chen Wu, Bo Du, Liangpei Zhang
Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most end-to-end networks are proposed for supervised change detection,…
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling
Yang Long, Gui-Song Xia, Liangpei Zhang +2
Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure of the image content, e.g., by assigning a semantic label to every pixel of the image.…