1 code implementation • 8 May 2024 • Kaiyu Li, Xiangyong Cao, Yupeng Deng, Deyu Meng
Motivated by this, we first propose a VLM-based mixed change event generation (CEG) strategy to yield pseudo labels for unlabeled CD data.
no code implementations • 16 Apr 2024 • Haodong Wen, Bodong Du, Ruixun Liu, Deyu Meng, Xiangyong Cao
Subsequently, PNCA is used to analyze the mainstream polynomial filters, and a novel simple basis that decouples the positive and negative activation and fully utilizes graph structure information is designed.
1 code implementation • 8 Apr 2024 • Shihong Wang, Ruixun Liu, Kaiyu Li, Jiawei Jiang, Xiangyong Cao
This paper focuses on the relevance between base and novel classes, and improves GFSS in two aspects: 1) mining the similarity between base and novel classes to promote the learning of novel classes, and 2) mitigating the class imbalance issue caused by the volume difference between the support set and the training set.
1 code implementation • 18 Mar 2024 • Datao Tang, Xiangyong Cao, Xingsong Hou, Zhongyuan Jiang, Deyu Meng
The emergence of diffusion models has revolutionized the field of image generation, providing new methods for creating high-quality, high-resolution images across various applications.
1 code implementation • 24 Feb 2024 • Li Pang, Xiangyu Rui, Long Cui, Hongzhong Wang, Deyu Meng, Xiangyong Cao
Specifically, the reduced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled.
2 code implementations • 2 Dec 2023 • Kaiyu Li, Xiangyong Cao, Deyu Meng
Change detection (CD) is a critical task to observe and analyze dynamic processes of land cover.
Building change detection for remote sensing images Change Detection +1
1 code implementation • 31 Aug 2023 • Shuang Xu, Yifan Wang, Zixiang Zhao, Jiangjun Peng, Xiangyong Cao, Deyu Meng, Yulun Zhang, Radu Timofte, Luc van Gool
NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer.
no code implementations • 16 Jun 2023 • Yuntao Shou, Xiangyong Cao, Deyu Meng, Bo Dong, Qinghua Zheng
By setting a matching weight and calculating attention scores between modal features row by row, LMAM contains fewer parameters than the self-attention method.
no code implementations • 19 May 2023 • Yingchun Wang, Jingcai Guo, Yi Liu, Song Guo, Weizhan Zhang, Xiangyong Cao, Qinghua Zheng
Based on the idea that in-distribution (ID) data with spurious features may have a lower experience risk, in this paper, we propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures without referring to the above drawbacks.
1 code implementation • 18 May 2023 • Xiangyu Rui, Xiangyong Cao, Li Pang, Zeyu Zhu, Zongsheng Yue, Deyu Meng
To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization ability of Bayesian methods.
no code implementations • ICCV 2023 • Gang Yang, Xiangyong Cao, Wenzhe Xiao, Man Zhou, Aiping Liu, Xun Chen, Deyu Meng
The experimental results verify that the proposed PanFlowNet can generate various HRMS images given an LRMS image and a PAN image.
1 code implementation • CVPR 2023 • Zeyu Zhu, Xiangyong Cao, Man Zhou, Junhao Huang, Deyu Meng
Pansharpening is an essential preprocessing step for remote sensing image processing.
no code implementations • 9 Dec 2022 • Xiangyu Rui, Xiangyong Cao, Jun Shu, Qian Zhao, Deyu Meng
Extensive experiments verify that the proposed HWnet can help improve the generalization ability of a weighted model to adapt to more complex noise, and can also strengthen the weighted model by transferring the knowledge from another weighted model.
no code implementations • 3 Nov 2022 • Jiangjun Peng, Hailin Wang, Xiangyong Cao, Xinlin Liu, Xiangyu Rui, Deyu Meng
The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data $ \mathbf{X} \in \mathbb{R}^{MN\times B}$.
no code implementations • 12 Feb 2022 • Xiangyong Cao, Yang Chen, Wenfei Cao
To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method.
no code implementations • 12 Feb 2022 • Man Zhou, Keyu Yan, Jinshan Pan, Wenqi Ren, Qi Xie, Xiangyong Cao
Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image.
no code implementations • CVPR 2021 • Xiangyu Rui, Xiangyong Cao, Qi Xie, Zongsheng Yue, Qian Zhao, Deyu Meng
A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements.
no code implementations • 5 Aug 2020 • Haixia Bi, Lin Xu, Xiangyong Cao, Yong Xue, Zongben Xu
Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications.
no code implementations • 16 Jun 2020 • Jiacheng Sun, Xiangyong Cao, Hanwen Liang, Weiran Huang, Zewei Chen, Zhenguo Li
In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc.
1 code implementation • 13 Sep 2019 • Minghan Li, Xiangyong Cao, Qian Zhao, Lei Zhang, Chenqiang Gao, Deyu Meng
Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence.
1 code implementation • 6 Sep 2018 • Shiqi Liu, Jingxin Liu, Qian Zhao, Xiangyong Cao, Huibin Li, Hongy-ing Meng, Sheng Liu, Deyu Meng
In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks.
no code implementations • ICLR 2018 • Shiqi Liu, Qian Zhao, Xiangyong Cao, Deyu Meng, Zilu Ma, Tao Yu
This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.
1 code implementation • 1 May 2017 • Xiangyong Cao, Feng Zhou, Lin Xu, Deyu Meng, Zongben Xu, John Paisley
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework.
Ranked #13 on Hyperspectral Image Classification on Indian Pines (Overall Accuracy metric, using extra training data)
no code implementations • 1 Feb 2017 • Yang Chen, Xiangyong Cao, Qian Zhao, Deyu Meng, Zongben Xu
In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i. i. d.
no code implementations • ICCV 2015 • Xiangyong Cao, Qian Zhao, Deyu Meng, Yang Chen, Zongben Xu
Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data.