1 code implementation • 4 May 2024 • Andong Lu, Wanyu Wang, Chenglong Li, Jin Tang, Bin Luo
In particular, we design a fusion structure space based on the hierarchical attention network, each attention-based fusion unit corresponding to a fusion operation and a combination of these attention units corresponding to a fusion structure.
Ranked #1 on Rgb-T Tracking on RGBT210
1 code implementation • 27 Apr 2024 • Xiao Wang, Yuehang Li, Wentao Wu, Jiandong Jin, Yao Rong, Bo Jiang, Chuanfu Li, Jin Tang
Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e. g., 224 $\times$ 224).
1 code implementation • 15 Apr 2024 • Xiao Wang, Shiao Wang, Yuhe Ding, Yuehang Li, Wentao Wu, Yao Rong, Weizhe Kong, Ju Huang, Shihao Li, Haoxiang Yang, Ziwen Wang, Bo Jiang, Chenglong Li, YaoWei Wang, Yonghong Tian, Jin Tang
In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM.
4 code implementations • 9 Mar 2024 • Xiao Wang, Ju Huang, Shiao Wang, Chuanming Tang, Bo Jiang, Yonghong Tian, Jin Tang, Bin Luo
Current event-/frame-event based trackers undergo evaluation on short-term tracking datasets, however, the tracking of real-world scenarios involves long-term tracking, and the performance of existing tracking algorithms in these scenarios remains unclear.
1 code implementation • 20 Jan 2024 • Haoxiang Yang, Chengguo Yuan, Yabin Zhu, Lan Chen, Xiao Wang, Jin Tang
The mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e. g., low illumination, motion blur).
1 code implementation • 10 Jan 2024 • Haobo Yue, Zhicheng Zhang, Da Mu, Yonghao Dang, Jianqin Yin, Jin Tang
Recently, 2D convolution has been found unqualified in sound event detection (SED).
no code implementations • 8 Jan 2024 • Ziyan Zhang, Bo Jiang, Jin Tang, Bin Luo
Based on the proposed GMA, we then propose a unified graph contrastive learning, termed Graph Message Contrastive Learning (GMCL), that employs attribution-guided universal GMA for graph contrastive learning.
1 code implementation • 5 Jan 2024 • Yabin Zhu, Xiao Wang, Chenglong Li, Bo Jiang, Lin Zhu, Zhixiang Huang, Yonghong Tian, Jin Tang
In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras.
no code implementations • 25 Dec 2023 • Andong Lu, Tianrui Zha, Chenglong Li, Jin Tang, XiaoFeng Wang, Bin Luo
To perform effective collaborative modeling between image relighting and person ReID tasks, we integrate the multilevel feature interactions in CENet.
1 code implementation • 25 Dec 2023 • Andong Lu, jiacong Zhao, Chenglong Li, Jin Tang, Bin Luo
To address this challenge, we propose a novel invertible prompt learning approach, which integrates the content-preserving prompts into a well-trained tracking model to adapt to various modality-missing scenarios, for robust RGBT tracking.
1 code implementation • 22 Dec 2023 • Lei Liu, Mengya Zhang, Cheng Li, Chenglong Li, Jin Tang
Visual tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences.
2 code implementations • 22 Dec 2023 • Shinan Zou, Chao Fan, Jianbo Xiong, Chuanfu Shen, Shiqi Yu, Jin Tang
Compared to existing datasets, CCGR has both population and individual-level diversity.
1 code implementation • 22 Dec 2023 • Shinan Zou, Jianbo Xiong, Chao Fan, Shiqi Yu, Jin Tang
In this paper, by considering the temporal and spatial characteristics of gait data, we propose a multi-stage feature fusion strategy (MSFFS), which performs multimodal fusions at different stages in the feature extraction process.
1 code implementation • 22 Dec 2023 • Lei Liu, Chenglong Li, Futian Wang, Longfeng Shen, Jin Tang
In particular, we design a multi-modal prototype to represent target information by multi-kind samples, including a fixed sample from the first frame and two representative samples from different modalities.
1 code implementation • 18 Dec 2023 • Xiao Wang, Yao Rong, Shiao Wang, Yuan Chen, Zhe Wu, Bo Jiang, Yonghong Tian, Jin Tang
It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig.~\ref{firstimage}.
2 code implementations • 17 Dec 2023 • Xiao Wang, Jiandong Jin, Chenglong Li, Jin Tang, Cheng Zhang, Wei Wang
In this paper, we formulate PAR as a vision-language fusion problem and fully exploit the relations between pedestrian images and attribute labels.
1 code implementation • 15 Dec 2023 • Xiao Wang, Wentao Wu, Chenglong Li, Zhicheng Zhao, Zhe Chen, Yukai Shi, Jin Tang
To address this issue, we propose a novel vehicle-centric pre-training framework called VehicleMAE, which incorporates the structural information including the spatial structure from vehicle profile information and the semantic structure from informative high-level natural language descriptions for effective masked vehicle appearance reconstruction.
2 code implementations • 4 Dec 2023 • Jiandong Jin, Xiao Wang, Chenglong Li, Lili Huang, Jin Tang
Then, a Transformer decoder is proposed to generate the human attributes by incorporating the visual features and attribute query tokens.
4 code implementations • 26 Sep 2023 • Xiao Wang, Shiao Wang, Chuanming Tang, Lin Zhu, Bo Jiang, Yonghong Tian, Jin Tang
Tracking using bio-inspired event cameras has drawn more and more attention in recent years.
1 code implementation • 31 Aug 2023 • Andong Lu, Zhang Zhang, Yan Huang, Yifan Zhang, Chenglong Li, Jin Tang, Liang Wang
The illumination enhancement branch first estimates an enhanced image from the nighttime image using a nonlinear curve mapping method and then extracts the enhanced features.
1 code implementation • 8 Aug 2023 • Xiao Wang, Zongzhen Wu, Yao Rong, Lin Zhu, Bo Jiang, Jin Tang, Yonghong Tian
Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition.
1 code implementation • 8 Jun 2023 • Bo Jiang, Chengguo Yuan, Xiao Wang, Zhimin Bao, Lin Zhu, Yonghong Tian, Jin Tang
To address these issues, we propose a novel dual point-voxel absorbing graph representation learning for event stream data representation.
1 code implementation • IEEE Transactions on Geoscience and Remote Sensing 2023 • Wei Lu, Si-Bao Chen, Jin Tang, Chris H. Q. Ding, and Bin Luo
To address this problem, we propose a new and universal downsampling module named robust feature downsampling (RFD).
no code implementations • 30 May 2023 • Bo Jiang, Shuxian Luo, Xiao Wang, Chuanfu Li, Jin Tang
Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem.
no code implementations • 25 May 2023 • Aihua Zheng, Chaobin Zhang, Weijun Zhang, Chenglong Li, Jin Tang, Chang Tan, Ruoran Jia
Existing vehicle re-identification methods mainly rely on the single query, which has limited information for vehicle representation and thus significantly hinders the performance of vehicle Re-ID in complicated surveillance networks.
no code implementations • 25 May 2023 • Aihua Zheng, Ziling He, Zi Wang, Chenglong Li, Jin Tang
Many existing multi-modality studies are based on the assumption of modality integrity.
no code implementations • 12 May 2023 • Bo Jiang, Fei Xu, Ziyan Zhang, Jin Tang, Feiping Nie
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning.
no code implementations • 26 Mar 2023 • Yabin Zhu, Chenglong Li, Xiao Wang, Jin Tang, Zhixiang Huang
In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy.
no code implementations • 31 Dec 2022 • Xiaofa Liu, Jianqin Yin, Yuan Sun, Zhicheng Zhang, Jin Tang
Unlike most existing methods with offline feature generation, our method directly takes frames as input and further models motion evolution on two different temporal scales. Therefore, we solve the complexity problems of the two stages of modeling and the problem of insufficient temporal and spatial information of a single scale.
1 code implementation • 8 Oct 2022 • Tao Zhong, Zhixiang Chi, Li Gu, Yang Wang, Yuanhao Yu, Jin Tang
Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains.
Ranked #22 on Domain Generalization on DomainNet
no code implementations • 25 Sep 2022 • Chenglong Li, Qiwen Zhu, Tubiao Liu, Jin Tang, Yu Su
To address this issue, we design a multi-stage convolution-transformer network for step segmentation.
1 code implementation • 22 Sep 2022 • Xue Yang, Gefan Zhang, Xiaojiang Yang, Yue Zhou, Wentao Wang, Jin Tang, Tao He, Junchi Yan
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects.
1 code implementation • 1 Aug 2022 • Aihua Zheng, Xianpeng Zhu, Zhiqi Ma, Chenglong Li, Jin Tang, Jixin Ma
In particular, we design a new cross-directional center loss to pull the modality centers of each identity close to mitigate cross-modality discrepancy, while the sample centers of each identity close to alleviate the sample discrepancy.
no code implementations • 25 Jul 2022 • Zhixiang Chi, Rasoul Mohammadi Nasiri, Zheng Liu, Yuanhao Yu, Juwei Lu, Jin Tang, Konstantinos N Plataniotis
However, none of the published VFI works considers the spatially non-uniform characteristics of the interpolation error (IE).
1 code implementation • 22 Jul 2022 • Huan Liu, Li Gu, Zhixiang Chi, Yang Wang, Yuanhao Yu, Jun Chen, Jin Tang
In this paper, we show through empirical results that adopting the data replay is surprisingly favorable.
no code implementations • 2 Jun 2022 • Chenglong Li, Xiaobin Yang, Guohao Wang, Aihua Zheng, Chang Tan, Ruoran Jia, Jin Tang
License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few.
1 code implementation • 19 May 2022 • Xiao Wang, Zhe Chen, Bo Jiang, Jin Tang, Bin Luo, DaCheng Tao
To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame.
no code implementations • 8 May 2022 • Tingxiu Chen, Jianqin Yin, Jin Tang
In recent years, audio-visual event localization has attracted much attention.
1 code implementation • 11 Feb 2022 • Yabin Zhu, Chenglong Li, Yao Liu, Xiao Wang, Jin Tang, Bin Luo, Zhixiang Huang
Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection and segmentation.
2 code implementations • AAAI2022 2022 • Yun Xiao, Mengmeng Yang, Chenglong Li, Lei Liu, Jin Tang
RGBT tracking usually suffers from various challenging factors of fast motion, scale variation, illumination variation, thermal crossover and occlusion, to name a few.
Ranked #5 on Rgb-T Tracking on GTOT
no code implementations • CVPR 2022 • Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, Jin Tang
The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i. e. incrementally learning new classes) during testing.
no code implementations • 2 Dec 2021 • Xixi Wang, Xiao Wang, Bo Jiang, Jin Tang, Bin Luo
In this work, we re-think Transformer and extend it to MutualFormer for multi-modality data representation.
no code implementations • MM 2021 • Bo Jiang, Pengfei Sun, Ziyan Zhang, Jin Tang, Bin Luo
Also, GAMnet exploits sparse GM optimization as correspondence solver which is differentiable and can also incorporate discrete one-to-one matching constraints approximately in natural in the final matching prediction.
Ranked #7 on Graph Matching on PASCAL VOC (matching accuracy metric)
no code implementations • CVPR 2021 • Zhixiang Chi, Yang Wang, Yuanhao Yu, Jin Tang
Therefore, we are able to exploit the internal information at test time via the auxiliary task to enhance the performance of deblurring.
1 code implementation • 9 Jun 2021 • Xiao Wang, Jin Tang, Bin Luo, YaoWei Wang, Yonghong Tian, Feng Wu
In this paper, we propose a novel and general target-aware attention mechanism (termed TANet) and integrate it with tracking-by-detection framework to conduct joint local and global search for robust tracking.
1 code implementation • 27 Apr 2021 • Chenglong Li, Wanlin Xue, Yaqing Jia, Zhichen Qu, Bin Luo, Jin Tang, Dengdi Sun
RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods.
no code implementations • 11 Apr 2021 • Jin Tang, Jin Zhang, Jianqin Yin
In this paper, we propose a novel temporal fusion (TF) module to fuse the two-stream joints' information to predict human motion, including a temporal concatenation and a reinforcement trajectory spatial-temporal (TST) block, specifically designed to keep prediction temporal consistency.
1 code implementation • 30 Mar 2021 • Xiao Wang, Zhe Chen, Jin Tang, Bin Luo, YaoWei Wang, Yonghong Tian, Feng Wu
In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy.
no code implementations • 19 Jan 2021 • Jie Wang, Zhaoxia Yin, Jin Tang, Jing Jiang, Bin Luo
The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs).
no code implementations • 15 Jan 2021 • Xiangyuan Zhu, Xiaoming Xiao, Tardi Tjahjadi, Zhihu Wu, Jin Tang
Image enhancement aims at processing an input image so that the visual content of the output image is more pleasing or more useful for certain applications.
no code implementations • 18 Nov 2020 • Aihua Zheng, Xia Sun, Chenglong Li, Jin Tang
Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.
no code implementations • 14 Nov 2020 • Andong Lu, Cun Qian, Chenglong Li, Jin Tang, Liang Wang
To deal with the tracking failure caused by sudden camera motion, which often occurs in RGBT tracking, we design a resampling strategy based on optical flow algorithms.
Ranked #19 on Rgb-T Tracking on RGBT234
no code implementations • 14 Nov 2020 • Andong Lu, Chenglong Li, Yuqing Yan, Jin Tang, Bin Luo
In specific, we use the modified VGG-M as the generality adapter to extract the modality-shared target representations. To extract the modality-specific features while reducing the computational complexity, we design a modality adapter, which adds a small block to the generality adapter in each layer and each modality in a parallel manner.
Ranked #9 on Rgb-T Tracking on GTOT
no code implementations • ECCV 2020 • Chenglong Li, Lei Liu, Andong Lu, Qing Ji, Jin Tang
RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking.
Ranked #8 on Rgb-T Tracking on GTOT
no code implementations • ECCV 2020 • Zhixiang Chi, Rasoul Mohammadi Nasiri, Zheng Liu, Juwei Lu, Jin Tang, Konstantinos N. Plataniotis
Recent advances in high refresh rate displays as well as the increased interest in high rate of slow motion and frame up-conversion fuel the demand for efficient and cost-effective multi-frame video interpolation solutions.
2 code implementations • 5 Jun 2020 • Zhengzheng Tu, Zhun Li, Chenglong Li, Yang Lang, Jin Tang
RGBT salient object detection (SOD) aims to segment the common prominent regions of visible and thermal infrared images.
2 code implementations • 5 May 2020 • Zhengzheng Tu, Zhun Li, Chenglong Li, Yang Lang, Jin Tang
Then, we design a novel dual-decoder to conduct the interactions of multi-level features, two modalities and global contexts.
5 code implementations • 28 Apr 2020 • Xue Yang, Junchi Yan, Wenlong Liao, Xiaokang Yang, Jin Tang, Tao He
Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
Ranked #33 on Object Detection In Aerial Images on DOTA (using extra training data)
no code implementations • 17 Mar 2020 • Zhengzheng Tu, Chun Lin, Chenglong Li, Jin Tang, Bin Luo
Classifying the confusing samples in the course of RGBT tracking is a quite challenging problem, which hasn't got satisfied solution.
no code implementations • 1 Mar 2020 • Sulan Zhai, Shunqiang Liu, Xiao Wang, Jin Tang
Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification.
1 code implementation • 21 Dec 2019 • Bo Jiang, Zitai Zhou, Xiao Wang, Jin Tang, Bin Luo
Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem.
no code implementations • 24 Nov 2019 • Bo Jiang, Xixi Wang, Jin Tang
Discriminative feature representation of person image is important for person re-identification (Re-ID) task.
no code implementations • 18 Nov 2019 • Bo Jiang, Pengfei Sun, Jin Tang, Bin Luo
However, the matching graphs we feed to existing graph convolutional matching networks are generally fixed and independent of graph matching, which thus are not guaranteed to be optimal for the graph matching task.
Ranked #15 on Graph Matching on Willow Object Class
no code implementations • 4 Sep 2019 • Bo Jiang, Leiling Wang, Jin Tang, Bin Luo
In particular, CaGAT conducts context-aware learning on both node feature representation and edge (weight) representation simultaneously and cooperatively in a unified manner which can boost their respective performance in network training.
no code implementations • 4 Sep 2019 • Bo Jiang, Beibei Wang, Jin Tang, Bin Luo
Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks.
no code implementations • 14 Aug 2019 • Bo Jiang, Leiling Wang, Jin Tang, Bin Luo
In this paper, we first re-interpret graph convolution operation in GCNs as a composition of feature propagation and (non-linear) transformation.
no code implementations • 12 Aug 2019 • Rui Yang, Yabin Zhu, Xiao Wang, Chenglong Li, Jin Tang
RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data.
no code implementations • 7 Aug 2019 • Zhengzheng Tu, Yan Ma, Chenglong Li, Jin Tang, Bin Luo
To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge guided feature learning for accurate salient object detection.
no code implementations • 5 Aug 2019 • Chenglong Li, Yan Huang, Liang Wang, Jin Tang, Liang Lin
Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected.
no code implementations • 24 Jul 2019 • Yabin Zhu, Chenglong Li, Bin Luo, Jin Tang, Xiao Wang
In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way.
1 code implementation • 24 Jul 2019 • Chenglong Li, Wei Xia, Yan Yan, Bin Luo, Jin Tang
These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night.
no code implementations • 17 Jul 2019 • Chenglong Li, Andong Lu, Aihua Zheng, Zhengzheng Tu, Jin Tang
In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object.
1 code implementation • 16 May 2019 • Zhengzheng Tu, Tian Xia, Chenglong Li, Xiaoxiao Wang, Yan Ma, Jin Tang
In this paper, we propose an effective approach for RGB-T image saliency detection.
no code implementations • 6 May 2019 • Xiao Wang, Ziliang Chen, Rui Yang, Bin Luo, Jin Tang
In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification.
no code implementations • 22 Jan 2019 • Bo Jiang, Ziyan Zhang, Jin Tang, Bin Luo
In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning.
1 code implementation • 22 Jan 2019 • Xiao Wang, Shaofei Zheng, Rui Yang, Aihua Zheng, Zhe Chen, Jin Tang, Bin Luo
We also review some popular network architectures which have been widely applied in the deep learning community.
no code implementations • 27 Nov 2018 • Xiao Wang, Tao Sun, Rui Yang, Chenglong Li, Bin Luo, Jin Tang
In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL).
no code implementations • 25 Nov 2018 • Xiao Wang, Chenglong Li, Rui Yang, Tianzhu Zhang, Jin Tang, Bin Luo
To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues.
no code implementations • 25 Nov 2018 • Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang
Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks.
no code implementations • 24 Nov 2018 • Yabin Zhu, Chenglong Li, Bin Luo, Jin Tang
This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking).
no code implementations • 1 Oct 2018 • Bo Jiang, Doudou Lin, Jin Tang
We present a novel graph diffusion-embedding networks (GDEN) for graph structured data.
1 code implementation • ECCV 2018 • Chenglong Li, Chengli Zhu, Yan Huang, Jin Tang, Liang Wang
To address this problem, this paper presents a novel approach to suppress background effects for RGB-T tracking.
no code implementations • CVPR 2018 • Xiao Wang, Chenglong Li, Bin Luo, Jin Tang
Based on the generated hard positive samples, we train a Siamese network for visual tracking and our experiments validate the effectiveness of the introduced algorithm.
no code implementations • 23 May 2018 • Chenglong Li, Xinyan Liang, Yijuan Lu, Nan Zhao, Jin Tang
RGB-Thermal (RGB-T) object tracking receives more and more attention due to the strongly complementary benefits of thermal information to visible data.
no code implementations • 17 Apr 2018 • Bo Jiang, Doudou Lin, Bin Luo, Jin Tang
To address this problem, we propose a novel unified temporal coherence and graph optimized ranking model for weighted patch representation in visual tracking problem.
no code implementations • NeurIPS 2017 • Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo
As a fundamental problem in computer vision, graph matching problem can usually be formulated as a Quadratic Programming (QP) problem with doubly stochastic and discrete (integer) constraints.
no code implementations • 4 Oct 2017 • Chenglong Li, Liang Lin, WangMeng Zuo, Jin Tang, Ming-Hsuan Yang
First, the graph is initialized by assigning binary weights of some image patches to indicate the object and background patches according to the predicted bounding box.
no code implementations • CVPR 2017 • Bo Jiang, Jin Tang, Chris Ding, Bin Luo
There are three main contributions of the proposed method: (1) we propose a new graph matching relaxation model, called Binary Constraint Preserving Graph Matching (BPGM), which aims to incorporate the discrete binary mapping constraints more in graph matching relaxation.
1 code implementation • 11 Jan 2017 • Chenglong Li, Guizhao Wang, Yunpeng Ma, Aihua Zheng, Bin Luo, Jin Tang
In particular, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking algorithm to achieve adaptive fusion of different source data.
no code implementations • CVPR 2015 • Chenglong Li, Liang Lin, WangMeng Zuo, Shuicheng Yan, Jin Tang
In particular, the affinity matrix with the rank fixed can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions.
no code implementations • CVPR 2013 • Bo Jiang, Chris Ding, Bio Luo, Jin Tang
Principal Component Analysis (PCA) is a widely used to learn a low-dimensional representation.