no code implementations • 2 May 2024 • Guanyiman Fu, Fengchao Xiong, Jianfeng Lu, Jun Zhou, Yuntao Qian
We can obtain complete global spatial-spectral correlation within a module thanks to the linear space complexity in State Space Model (SSM) computations.
1 code implementation • 31 Dec 2023 • Guanyiman Fu, Fengchao Xiong, Jianfeng Lu, Jun Zhou, Jiantao Zhou, Yuntao Qian
This block consists of a spatial branch and a spectral branch.
no code implementations • 3 Aug 2023 • Peng Wang, Fanwei Zeng, Yuntao Qian
Spatio-temporal action detection (STAD) aims to classify the actions present in a video and localize them in space and time.
no code implementations • 22 Nov 2022 • Peng Wang, Jingzhou Chen, Yuntao Qian
Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure.
1 code implementation • CVPR 2022 • Jingzhou Chen, Peng Wang, Jian Liu, Yuntao Qian
Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e. g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels.
no code implementations • 22 Nov 2021 • Peng Wang, Jun Wen, Chenyang Si, Yuntao Qian, Liang Wang
Finally, in the Information Fuser, we explore varied strategies to combine the Sequence Reconstructor and Contrastive Motion Learner, and propose to capture postures and motions simultaneously via a knowledge-distillation based fusion strategy that transfers the motion learning from the Contrastive Motion Learner to the Sequence Reconstructor.
1 code implementation • 1 Nov 2021 • Ling Chen, Jun Cui, Xing Tang, Chaodu Song, Yuntao Qian, Yansheng Li, Yongjun Zhang
Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings.
1 code implementation • 27 Aug 2021 • Ling Chen, Jiahui Xu, Binqing Wu, Yuntao Qian, Zhenhong Du, Yansheng Li, Yongjun Zhang
The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively.
no code implementations • 3 Dec 2020 • Fengchao Xiong, Shuyin Tao, Jun Zhou, Jianfeng Lu, Jiantao Zhou, Yuntao Qian
This model first projects the observed HSIs into a low-dimensional orthogonal subspace, and then represents the projected image with a multidimensional dictionary.
no code implementations • 16 Apr 2019 • Zhijian Luo, Siyu Chen, Yuntao Qian
In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy.
no code implementations • 16 Apr 2019 • Jingzhou Chen, Siyu Chen, Peilin Zhou, Yuntao Qian
Secondly, in order to utilize the local spatial correlation among pixels, we share the previous subnetwork as a spectral feature extractor for each pixel in a patch of image, after which the spectral features of all pixels in a patch are combined and feeded into the subsequent classification subnetwork.
no code implementations • 11 Dec 2018 • Fengchao Xiong, Jun Zhou, Yuntao Qian
Traditional color images only depict color intensities in red, green and blue channels, often making object trackers fail in challenging scenarios, e. g., background clutter and rapid changes of target appearance.
no code implementations • 7 Feb 2018 • Siyu Chen, Danping Liao, Yuntao Qian
The adversarial loss pushes our solution to the natural image distribution using a discriminator network that is trained to differentiate between false-color images and natural-color images.
no code implementations • 20 Jan 2018 • Danping Liao, Siyu Chen, Yuntao Qian
The matching pixels between a pair of HSI and RGB image can be reused to display other HSIs captured b the same imaging sensor with natural colors.
no code implementations • 24 Nov 2017 • Danping Liao, Yuntao Qian, Yuan Yan Tang
A composite kernel is applied in manifold learning to incorporate both the spatial and spectral information of HSI in the embedded space.
no code implementations • 19 May 2016 • Jie Liang, Jun Zhou, Yuntao Qian, Lian Wen, Xiao Bai, Yongsheng Gao
Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification.