1 code implementation • 16 May 2024 • Zhaoxu Li, Wei An, Gaowei Guo, Longguang Wang, Yingqian Wang, Zaiping Lin
We develop a simulated hyperSpectral Point Object Detection benchmark termed SPOD, and for the first time, evaluate and compare the performance of current object detection networks and HTD methods on hyperspectral multi-class point object detection.
1 code implementation • 7 May 2024 • DeepSeek-AI, Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Hanwei Xu, Hao Yang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, JianZhong Guo, Jiaqi Ni, Jiashi Li, Jin Chen, Jingyang Yuan, Junjie Qiu, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruizhe Pan, Runxin Xu, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shirong Ma, Shiyu Wang, Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Size Zheng, T. Wang, Tian Pei, Tian Yuan, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Liu, Xin Xie, Xingkai Yu, Xinnan Song, Xinyi Zhou, Xinyu Yang, Xuan Lu, Xuecheng Su, Y. Wu, Y. K. Li, Y. X. Wei, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang, Yi Zheng, Yichao Zhang, Yiliang Xiong, Yilong Zhao, Ying He, Ying Tang, Yishi Piao, Yixin Dong, Yixuan Tan, Yiyuan Liu, Yongji Wang, Yongqiang Guo, Yuchen Zhu, Yuduan Wang, Yuheng Zou, Yukun Zha, Yunxian Ma, Yuting Yan, Yuxiang You, Yuxuan Liu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhewen Hao, Zhihong Shao, Zhiniu Wen, Zhipeng Xu, Zhongyu Zhang, Zhuoshu Li, Zihan Wang, Zihui Gu, Zilin Li, Ziwei Xie
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
1 code implementation • ICCV 2023 • Boyang Li, Yingqian Wang, Longguang Wang, Fei Zhang, Ting Liu, Zaiping Lin, Wei An, Yulan Guo
The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.
1 code implementation • 31 Mar 2023 • Zhaoxu Li, Yingqian Wang, Chao Xiao, Qiang Ling, Zaiping Lin, Wei An
Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way.
1 code implementation • 28 Sep 2022 • Tianhao Wu, Boyang Li, Yihang Luo, Yingqian Wang, Chao Xiao, Ting Liu, Jungang Yang, Wei An, Yulan Guo
Due to the extremely large image coverage area (e. g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices.
3 code implementations • 13 Jun 2022 • Yingqian Wang, Zhengyu Liang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
In our method, a practical LF degradation model is developed to formulate the degradation process of real LF images.
1 code implementation • CVPR 2022 • Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Wei An, Yulan Guo
Based on the proposed cost constructor, we develop a deep network for LF depth estimation.
no code implementations • 22 Feb 2022 • Yingqian Wang, Longguang Wang, Gaochang Wu, Jungang Yang, Wei An, Jingyi Yu, Yulan Guo
In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing.
1 code implementation • CVPR 2022 • Longguang Wang, Xiaoyu Dong, Yingqian Wang, Li Liu, Wei An, Yulan Guo
Since a linear quantizer (i. e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e. g., exponential function) with learnable parameters to build the quantizer for joint optimization.
1 code implementation • 25 Nov 2021 • Qian Yin, Qingyong Hu, Hao liu, Feng Zhang, Yingqian Wang, Zaiping Lin, Wei An, Yulan Guo
Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.
no code implementations • 23 Oct 2021 • Yu Mo, Yingqian Wang, Chao Xiao, Jungang Yang, Wei An
Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available.
1 code implementation • 9 Aug 2021 • Yingqian Wang, Jungang Yang, Yulan Guo, Chao Xiao, Wei An
In this letter, we propose a light field refocusing method to improve the imaging quality of camera arrays.
1 code implementation • 1 Jun 2021 • Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao Li, Wei An, Yulan Guo
With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained.
1 code implementation • 31 May 2021 • Ting Liu, Jungang Yang, Boyang Li, Chao Xiao, Yang Sun, Yingqian Wang, Wei An
Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection.
2 code implementations • CVPR 2021 • Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, Yulan Guo
In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.
1 code implementation • 7 Nov 2020 • Yingqian Wang, Xinyi Ying, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used.
1 code implementation • 16 Sep 2020 • Longguang Wang, Yulan Guo, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An
Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.
1 code implementation • 7 Jul 2020 • Yingqian Wang, Jungang Yang, Longguang Wang, Xinyi Ying, Tianhao Wu, Wei An, Yulan Guo
In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.
1 code implementation • CVPR 2021 • Longguang Wang, Xiaoyu Dong, Yingqian Wang, Xinyi Ying, Zaiping Lin, Wei An, Yulan Guo
Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.
2 code implementations • ICCV 2021 • Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
In this paper, we propose to learn a scale-arbitrary image SR network from scale-specific networks.
1 code implementation • 6 Apr 2020 • Xinyi Ying, Longguang Wang, Yingqian Wang, Weidong Sheng, Wei An, Yulan Guo
In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR.
2 code implementations • 6 Jan 2020 • Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, Wei An
The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.
Ranked #6 on Video Super-Resolution on MSU Super-Resolution for Video Compression (BSQ-rate over ERQA metric)
1 code implementation • 17 Dec 2019 • Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Jingyi Yu, Yulan Guo
Specifically, spatial and angular features are first separately extracted from input LFs, and then repetitively interacted to progressively incorporate spatial and angular information.
1 code implementation • 10 Dec 2019 • Yingqian Wang, Tianhao Wu, Jungang Yang, Longguang Wang, Wei An, Yulan Guo
In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).
no code implementations • 15 Mar 2019 • Yingqian Wang, Longguang Wang, Jungang Yang, Wei An, Yulan Guo
With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs.
1 code implementation • CVPR 2019 • Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.
Ranked #1 on Image Super-Resolution on KITTI 2012 - 4x upscaling
2 code implementations • 23 Sep 2018 • Longguang Wang, Yulan Guo, Zaiping Lin, Xinpu Deng, Wei An
Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.
Ranked #13 on Video Super-Resolution on Vid4 - 4x upscaling
no code implementations • 23 Aug 2017 • Longguang Wang, Zaiping Lin, Jinyan Gao, Xinpu Deng, Wei An
Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications.
no code implementations • 20 Jun 2017 • Longguang Wang, Zaiping Lin, Xinpu Deng, Wei An
In this paper, we propose an end-to-end fast upscaling technique to replace the interpolation operator, design upscaling filters in LR space for periodic sub-locations respectively and shuffle the filter results to derive the final reconstruction errors in HR space.