2 code implementations • 11 Apr 2024 • Kai Luo, Yakun Ju, Lin Qi, Kaixuan Wang, Junyu Dong
Predicting accurate normal maps of objects from two-dimensional images in regions of complex structure and spatial material variations is challenging using photometric stereo methods due to the influence of surface reflection properties caused by variations in object geometry and surface materials.
no code implementations • 28 Sep 2023 • Shaoxiang Guo, Qing Cai, Lin Qi, Junyu Dong
In particular, the distribution order of hand joints in various 3D space directions is derived from pose labels, forming corresponding text prompts that are subsequently encoded into text representations.
1 code implementation • 8 Jul 2023 • Yuxuan Song, Xinyue Li, Lin Qi
In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module.
1 code implementation • 8 Jul 2023 • Dongyue Sun, Shiyao Jiang, Lin Qi
Existing edge-aware camouflaged object detection (COD) methods normally output the edge prediction in the early stage.
no code implementations • 22 Apr 2023 • Lin Qi, Xuewen Qin, Feng Gao, Junyu Dong, Xinbo Gao
To this end, we put forward a spatial attention weighted unmixing network, dubbed as SAWU-Net, which learns a spatial attention network and a weighted unmixing network in an end-to-end manner for better spatial feature exploitation.
no code implementations • 12 Mar 2022 • Lin Qi, Feng Gao, Junyu Dong, Xinbo Gao, Qian Du
Important findings on the use of spatial and spectral information in the autoencoder framework are discussed.
no code implementations • 22 Jun 2021 • Ying Gao, Xiaohan Feng, Tiange Zhang, Eric Rigall, Huiyu Zhou, Lin Qi, Junyu Dong
Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision.
no code implementations • 28 Feb 2021 • Lei Gao, Rui Zhang, Lin Qi, Enqing Chen, Ling Guan
The objective of multimodal information fusion is to mathematically analyze information carried in different sources and create a new representation which will be more effectively utilized in pattern recognition and other multimedia information processing tasks.
no code implementations • 28 Feb 2021 • Lei Gao, Lin Qi, Enqing Chen, Ling Guan
In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion.
no code implementations • 28 Feb 2021 • Lei Gao, Lin Qi, Ling Guan
The Fractional Fourier Transform (FRFT) has been playing a unique and increasingly important role in signal and image processing.
no code implementations • 27 Feb 2021 • Lei Gao, Lin Qi, Ling Guan
In this paper, we propose a novel discriminative model for online behavioral analysis with application to emotion state identification.
1 code implementation • 27 Jan 2021 • Peixiao Zheng, Xin Guo, Lin Qi
In this paper, we proposed an edge-labeling-based directed gated graph network (DGGN) for few-shot learning, which utilizes gated recurrent units to implicitly update the similarity between nodes.
no code implementations • 7 Jan 2021 • Aite Zhao, Junyu Dong, Jianbo Li, Lin Qi, Huiyu Zhou
It is a challenging task to identify a person based on her/his gait patterns.
1 code implementation • 7 Jan 2021 • Aite Zhao, Jianbo Li, Junyu Dong, Lin Qi, Qianni Zhang, Ning li, Xin Wang, Huiyu Zhou
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.
no code implementations • 29 Dec 2019 • Zhihao Lei, Lin Qi, Ying WEI, Yunlong Zhou
In this paper, we propose a dual aggregation network to adaptively aggregate different information in infant brain MRI segmentation.
no code implementations • 6 Jul 2018 • Yuanhong Xu, Pei Dong, Junyu Dong, Lin Qi
Obtaining dense 3D reconstrution with low computational cost is one of the important goals in the field of SLAM.
1 code implementation • 22 May 2017 • Mingyuan Jiu, Nelly Pustelnik, Stefan Janaqi, Mériam Chebre, Lin Qi, Philippe Ricoux
This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning.