no code implementations • 20 May 2022 • Licheng Tang, Yiyang Cai, Jiaming Liu, Zhibin Hong, Mingming Gong, Minhu Fan, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang
Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs.
no code implementations • 8 May 2022 • Harsha Vardhan Simhadri, George Williams, Martin Aumüller, Matthijs Douze, Artem Babenko, Dmitry Baranchuk, Qi Chen, Lucas Hosseini, Ravishankar Krishnaswamy, Gopal Srinivasa, Suhas Jayaram Subramanya, Jingdong Wang
The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold.
no code implementations • 27 Apr 2022 • Changyong Shu, Hemao Wu, Hang Zhou, Jiaming Liu, Zhibin Hong, Changxing Ding, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang
Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet.
no code implementations • 20 Apr 2022 • Desen Zhou, Zhichao Liu, Jian Wang, Leshan Wang, Tao Hu, Errui Ding, Jingdong Wang
To associate the predictions of disentangled decoders, we first generate a unified representation for HOI triplets with a base decoder, and then utilize it as input feature of each disentangled decoder.
no code implementations • 16 Apr 2022 • Shi Gong, Xiaoqing Ye, Xiao Tan, Jingdong Wang, Errui Ding, Yu Zhou, Xiang Bai
Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving for its powerful spatial representation ability.
1 code implementation • 14 Apr 2022 • Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
2 code implementations • 7 Apr 2022 • Mingyu Ding, Bin Xiao, Noel Codella, Ping Luo, Jingdong Wang, Lu Yuan
We show that these two self-attentions complement each other: (i) since each channel token contains an abstract representation of the entire image, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial locations, which in turn helps the global information modeling in channel attention.
Ranked #7 on
Image Classification
on ImageNet
(using extra training data)
1 code implementation • 6 Apr 2022 • Qiang Chen, Qiman Wu, Jian Wang, Qinghao Hu, Tao Hu, Errui Ding, Jian Cheng, Jingdong Wang
We propose MixFormer to find a solution.
no code implementations • 31 Mar 2022 • Mengjun Cheng, Yipeng Sun, Longchao Wang, Xiongwei Zhu, Kun Yao, Jie Chen, Guoli Song, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang
Visual appearance is considered to be the most important cue to understand images for cross-modal retrieval, while sometimes the scene text appearing in images can provide valuable information to understand the visual semantics.
Ranked #3 on
Cross-Modal Retrieval
on Flickr30k
no code implementations • 24 Feb 2022 • Yifan Liu, Chunhua Shen, Changqian Yu, Jingdong Wang
To this end, we perform inference at each frame.
no code implementations • 7 Feb 2022 • Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, Jingdong Wang
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised learning.
1 code implementation • NeurIPS 2021 • Yuhui Yuan, Rao Fu, Lang Huang, WeiHong Lin, Chao Zhang, Xilin Chen, Jingdong Wang
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
1 code implementation • NeurIPS 2021 • Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, Jingdong Wang
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
1 code implementation • 29 Oct 2021 • Yeshu Li, Jonathan Cui, Yilun Sheng, Xiao Liang, Jingdong Wang, Eric I-Chao Chang, Yan Xu
To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume.
1 code implementation • 18 Oct 2021 • Yuhui Yuan, Rao Fu, Lang Huang, WeiHong Lin, Chao Zhang, Xilin Chen, Jingdong Wang
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
no code implementations • 23 Aug 2021 • Jaebong Jeong, Janghun Jo, Jingdong Wang, Sunghyun Cho, Jaesik Park
Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color values for the input 3D scene.
1 code implementation • ICCV 2021 • Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang
Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention.
1 code implementation • 30 Jun 2021 • Yong Guo, Yaofo Chen, Mingkui Tan, Kui Jia, Jian Chen, Jingdong Wang
In practice, the convolutional operation on some of the windows (e. g., smooth windows that contain very similar pixels) can be very redundant and may introduce noises into the computation.
no code implementations • 13 Jun 2021 • Shaobo Min, Qi Dai, Hongtao Xie, Chuang Gan, Yongdong Zhang, Jingdong Wang
Cross-modal correlation provides an inherent supervision for video unsupervised representation learning.
1 code implementation • ICLR 2022 • Qi Han, Zejia Fan, Qi Dai, Lei Sun, Ming-Ming Cheng, Jiaying Liu, Jingdong Wang
Sparse connectivity: there is no connection across channels, and each position is connected to the positions within a small local window.
2 code implementations • CVPR 2021 • Xiaokang Chen, Yuhui Yuan, Gang Zeng, Jingdong Wang
Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image.
1 code implementation • NeurIPS 2021 • Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, Jingdong Wang
It stores the centroid points of the posting lists in the memory and the large posting lists in the disk.
6 code implementations • CVPR 2021 • Changqian Yu, Bin Xiao, Changxin Gao, Lu Yuan, Lei Zhang, Nong Sang, Jingdong Wang
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.
Ranked #22 on
Pose Estimation
on COCO test-dev
1 code implementation • CVPR 2021 • Zigang Geng, Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang
Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions.
1 code implementation • ICLR 2022 • Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang, Ping Luo
(4) Thorough studies of NCP on inter-, cross-, and intra-tasks highlight the importance of cross-task neural architecture design, i. e., multitask neural architectures and architecture transferring between different tasks.
no code implementations • 19 Mar 2021 • Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, Kun He
Various momentum iterative gradient-based methods are shown to be effective to improve the adversarial transferability.
1 code implementation • ICCV 2021 • Xiaosen Wang, Xuanran He, Jingdong Wang, Kun He
We investigate in this direction and observe that existing transformations are all applied on a single image, which might limit the adversarial transferability.
no code implementations • 1 Jan 2021 • Depu Meng, Zigang Geng, Zhirong Wu, Bin Xiao, Houqiang Li, Jingdong Wang
The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance.
no code implementations • 21 Sep 2020 • Dengpan Fu, Bo Xin, Jingdong Wang, Dong-Dong Chen, Jianmin Bao, Gang Hua, Houqiang Li
Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods.
1 code implementation • ICML 2020 • Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
Specifically, we discriminate texture from shape based on local self-information in an image, and adopt a Dropout-like algorithm to decorrelate the model output from the local texture.
1 code implementation • 10 Jul 2020 • Jianming Ye, Shiliang Zhang, Jingdong Wang
We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN.
4 code implementations • ECCV 2020 • Yuhui Yuan, Jingyi Xie, Xilin Chen, Jingdong Wang
We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model.
1 code implementation • ECCV 2020 • Fangyun Wei, Xiao Sun, Hongyang Li, Jingdong Wang, Stephen Lin
A recent approach for object detection and human pose estimation is to regress bounding boxes or human keypoints from a central point on the object or person.
1 code implementation • 28 Jun 2020 • Ke Sun, Zigang Geng, Depu Meng, Bin Xiao, Dong Liu, Zhao-Xiang Zhang, Jingdong Wang
The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping.
1 code implementation • CVPR 2020 • Baifeng Shi, Qi Dai, Yadong Mu, Jingdong Wang
By maximizing the conditional probability with respect to the attention, the action and non-action frames are well separated.
2 code implementations • CVPR 2020 • Yong Guo, Jian Chen, Jingdong Wang, Qi Chen, JieZhang Cao, Zeshuai Deng, Yanwu Xu, Mingkui Tan
Extensive experiments with paired training data and unpaired real-world data demonstrate our superiority over existing methods.
1 code implementation • ECCV 2020 • Yifan Liu, Chunhua Shen, Changqian Yu, Jingdong Wang
For semantic segmentation, most existing real-time deep models trained with each frame independently may produce inconsistent results for a video sequence.
Ranked #2 on
Video Semantic Segmentation
on CamVid
9 code implementations • ECCV 2020 • Yuhui Yuan, Xiaokang Chen, Xilin Chen, Jingdong Wang
We empirically demonstrate that the proposed approach achieves competitive performance on various challenging semantic segmentation benchmarks: Cityscapes, ADE20K, LIP, PASCAL-Context, and COCO-Stuff.
Ranked #2 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
1 code implementation • ICCV 2019 • Haibo Qiu, Chunyu Wang, Jingdong Wang, Naiyan Wang, Wen-Jun Zeng
It consists of two separate steps: (1) estimating the 2D poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses.
Ranked #3 on
3D Human Pose Estimation
on Total Capture
no code implementations • ICCV 2019 • Jianing Li, Jingdong Wang, Qi Tian, Wen Gao, Shiliang Zhang
The long-term relations are captured by a temporal self-attention model to alleviate the occlusions and noises in video sequences.
19 code implementations • CVPR 2020 • Bowen Cheng, Bin Xiao, Jingdong Wang, Honghui Shi, Thomas S. Huang, Lei Zhang
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
Ranked #2 on
Pose Estimation
on UAV-Human
31 code implementations • 20 Aug 2019 • Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
6 code implementations • 29 Jul 2019 • Lang Huang, Yuhui Yuan, Jianyuan Guo, Chao Zhang, Xilin Chen, Jingdong Wang
There are two successive attention modules each estimating a sparse affinity matrix.
144 code implementations • 17 Jun 2019 • Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin
In this paper, we introduce the various features of this toolbox.
no code implementations • 17 May 2019 • Weiyao Lin, Yuxi Li, Hao Xiao, John See, Junni Zou, Hongkai Xiong, Jingdong Wang, Tao Mei
The task of re-identifying groups of people underdifferent camera views is an important yet less-studied problem. Group re-identification (Re-ID) is a very challenging task sinceit is not only adversely affected by common issues in traditionalsingle object Re-ID problems such as viewpoint and human posevariations, but it also suffers from changes in group layout andgroup membership.
38 code implementations • 9 Apr 2019 • Ke Sun, Yang Zhao, Borui Jiang, Tianheng Cheng, Bin Xiao, Dong Liu, Yadong Mu, Xinggang Wang, Wenyu Liu, Jingdong Wang
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Ranked #4 on
Semantic Segmentation
on LIP val
1 code implementation • CVPR 2019 • Yifan Liu, Changyong Shun, Jingdong Wang, Chunhua Shen
Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem.
34 code implementations • CVPR 2019 • Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang
We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.
Ranked #1 on
Keypoint Detection
on COCO test-dev
no code implementations • CVPR 2016 • Xiaojuan Wang, Ting Zhang, Guo-Jun Q, Jinhui Tang, Jingdong Wang
In this paper, we address the problem of searching for semantically similar images from a large database.
no code implementations • CVPR 2016 • Ting Zhang, Jingdong Wang
Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e. g., using an image to search for texts or using a text to search for images.
1 code implementation • 1 Feb 2019 • Bin Liu, Yue Cao, Mingsheng Long, Jian-Min Wang, Jingdong Wang
We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets.
Ranked #1 on
Image Retrieval
on NUS-WIDE
no code implementations • 1 Jan 2019 • Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, Jingdong Wang
However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities.
no code implementations • NeurIPS 2018 • Xuguang Duan, Wenbing Huang, Chuang Gan, Jingdong Wang, Wenwu Zhu, Junzhou Huang
Dense event captioning aims to detect and describe all events of interest contained in a video.
no code implementations • 7 Sep 2018 • Junran Peng, Lingxi Xie, Zhao-Xiang Zhang, Tieniu Tan, Jingdong Wang
This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks.
8 code implementations • 4 Sep 2018 • Yuhui Yuan, Lang Huang, Jianyuan Guo, Chao Zhang, Xilin Chen, Jingdong Wang
To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}.
Ranked #9 on
Semantic Segmentation
on Trans10K
no code implementations • CVPR 2018 • Guotian Xie, Jingdong Wang, Ting Zhang, Jian-Huang Lai, Richang Hong, Guo-Jun Qi
In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels.
3 code implementations • 1 Jun 2018 • Ke Sun, Mingjie Li, Dong Liu, Jingdong Wang
In this paper, we are interested in building lightweight and efficient convolutional neural networks.
1 code implementation • CVPR 2018 • Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu, Jingdong Wang
Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing.
Ranked #21 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
(using extra training data)
no code implementations • ECCV 2018 • Yumin Suh, Jingdong Wang, Siyu Tang, Tao Mei, Kyoung Mu Lee
We propose a novel network that learns a part-aligned representation for person re-identification.
Ranked #4 on
Person Re-Identification
on UAV-Human
2 code implementations • 17 Apr 2018 • Guotian Xie, Jingdong Wang, Ting Zhang, Jian-Huang Lai, Richang Hong, Guo-Jun Qi
In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels.
no code implementations • 30 Jan 2018 • Peng Tang, Chunyu Wang, Xinggang Wang, Wenyu Liu, Wen-Jun Zeng, Jingdong Wang
In particular, our method improves results by 8. 8% over the static image detector for fast moving objects.
no code implementations • 20 Dec 2017 • Jianing Li, Shiliang Zhang, Jingdong Wang, Wen Gao, Qi Tian
This paper mainly establishes a large-scale Long sequence Video database for person re-IDentification (LVreID).
1 code implementation • 4 Dec 2017 • Jingdong Wang, Ting Zhang
We introduce a composite quantization framework.
1 code implementation • CVPR 2019 • Ruochen Fan, Ming-Ming Cheng, Qibin Hou, Tai-Jiang Mu, Jingdong Wang, Shi-Min Hu
Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch.
2 code implementations • CVPR 2018 • Guo-Jun Qi, Liheng Zhang, Hao Hu, Marzieh Edraki, Jingdong Wang, Xian-Sheng Hua
In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data.
no code implementations • ICCV 2017 • Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang
The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution.
no code implementations • ICCV 2017 • Song Bai, Zhichao Zhou, Jingdong Wang, Xiang Bai, Longin Jan Latecki, Qi Tian
This stimulates a great research interest of considering similarity fusion in the framework of diffusion process (i. e., fusion with diffusion) for robust retrieval.
no code implementations • ICCV 2017 • Ke Sun, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Dong Liu, Jingdong Wang
We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation.
no code implementations • 19 Sep 2017 • Gangming Zhao, Zhao-Xiang Zhang, He Guan, Peng Tang, Jingdong Wang
Most of convolutional neural networks share the same characteristic: each convolutional layer is followed by a nonlinear activation layer where Rectified Linear Unit (ReLU) is the most widely used.
1 code implementation • ICCV 2017 • Liming Zhao, Xi Li, Jingdong Wang, Yueting Zhuang
In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras.
Ranked #87 on
Person Re-Identification
on Market-1501
no code implementations • 19 Jul 2017 • Jingdong Wang, Yajie Xing, Kexin Zhang, Cha Zhang
Identity transformations, used as skip-connections in residual networks, directly connect convolutional layers close to the input and those close to the output in deep neural networks, improving information flow and thus easing the training.
2 code implementations • 10 Jul 2017 • Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang
The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution.
no code implementations • 20 Mar 2017 • Weiyao Lin, Yang shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu
We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.
4 code implementations • 23 Nov 2016 • Liming Zhao, Jingdong Wang, Xi Li, Zhuowen Tu, Wen-Jun Zeng
A deep residual network, built by stacking a sequence of residual blocks, is easy to train, because identity mappings skip residual branches and thus improve information flow.
no code implementations • 21 Jul 2016 • Lingxi Xie, Qi Tian, John Flynn, Jingdong Wang, Alan Yuille
For this, we consider the neurons in the hidden layer as neural words, and construct a set of geometric neural phrases on top of them.
no code implementations • 1 Jun 2016 • Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, Heng Tao Shen
In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations.
2 code implementations • 25 May 2016 • Jingdong Wang, Zhen Wei, Ting Zhang, Wen-Jun Zeng
Second, in our suggested fused net formed by one deep and one shallow base networks, the flows of the information from the earlier intermediate layer of the deep base network to the output and from the input to the later intermediate layer of the deep base network are both improved.
no code implementations • CVPR 2016 • Lingxi Xie, Liang Zheng, Jingdong Wang, Alan Yuille, Qi Tian
An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network.
2 code implementations • CVPR 2016 • Lingxi Xie, Jingdong Wang, Zhen Wei, Meng Wang, Qi Tian
During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc.
no code implementations • 1 Apr 2016 • Liang Zheng, Yali Zhao, Shengjin Wang, Jingdong Wang, Qi Tian
The objective of this paper is the effective transfer of the Convolutional Neural Network (CNN) feature in image search and classification.
no code implementations • 16 Feb 2016 • Weiyao Lin, Yang Mi, Weiyue Wang, Jianxin Wu, Jingdong Wang, Tao Mei
These semantic regions can be used to recognize pre-defined activities in crowd scenes.
no code implementations • ICCV 2015 • Lingxi Xie, Jingdong Wang, Weiyao Lin, Bo Zhang, Qi Tian
In many fine-grained object recognition datasets, image orientation (left/right) might vary from sample to sample.
no code implementations • ICCV 2015 • Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, Qi Tian
As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor.
Ranked #82 on
Person Re-Identification
on DukeMTMC-reID
no code implementations • 19 Oct 2015 • Xi Li, Liming Zhao, Lina Wei, Ming-Hsuan Yang, Fei Wu, Yueting Zhuang, Haibin Ling, Jingdong Wang
A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner.
no code implementations • CVPR 2015 • Ting Zhang, Guo-Jun Qi, Jinhui Tang, Jingdong Wang
The benefit is that the distance evaluation between the query and the dictionary element (a sparse vector) is accelerated using the efficient sparse vector operation, and thus the cost of distance table computation is reduced a lot.
no code implementations • CVPR 2015 • Dapeng Chen, Zejian yuan, Gang Hua, Nanning Zheng, Jingdong Wang
We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person.
no code implementations • CVPR 2015 • Dingwen Zhang, Junwei Han, Chao Li, Jingdong Wang
In the proposed framework, the wide and deep information are explored for the object proposal windows extracted in each image, and the co-saliency scores are calculated by integrating the intra-image contrast and intra group consistency via a principled Bayesian formulation.
1 code implementation • ICCV 2015 • Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.
no code implementations • 5 Jan 2015 • Jianfeng Wang, Shuicheng Yan, Yi Yang, Mohan S. Kankanhalli, Shipeng Li, Jingdong Wang
We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary.
no code implementations • CVPR 2013 • Huaizu Jiang, Zejian yuan, Ming-Ming Cheng, Yihong Gong, Nanning Zheng, Jingdong Wang
Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.
no code implementations • 18 Sep 2014 • Baoguang Shi, Xiang Bai, Wenyu Liu, Jingdong Wang
In this paper, we present a deep regression approach for face alignment.
no code implementations • 13 Aug 2014 • Jingdong Wang, Heng Tao Shen, Jingkuan Song, Jianqiu Ji
Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database.
no code implementations • 7 Aug 2014 • Chao Yang, Shengnan Caih, Jingdong Wang, Long Quan
As an extension of SIFT, our method seeks to add prior to solve the ill-posed affine parameter estimation problem and normalizes them directly, and is applicable to objects with regular structures.
no code implementations • 19 Jun 2014 • Chao Du, Jingdong Wang
This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach.
no code implementations • CVPR 2014 • Lingxi Xie, Jingdong Wang, Baining Guo, Bo Zhang, Qi Tian
The novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid.
no code implementations • 16 May 2014 • Jianfeng Wang, Jingdong Wang, Jingkuan Song, Xin-Shun Xu, Heng Tao Shen, Shipeng Li
In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace.
no code implementations • 11 Dec 2013 • Jingdong Wang, Jing Wang, Qifa Ke, Gang Zeng, Shipeng Li
Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center.
no code implementations • 11 Dec 2013 • Jingdong Wang, Jing Wang, Gang Zeng, Rui Gan, Shipeng Li, Baining Guo
This structure augments the neighborhood graph with a bridge graph.
no code implementations • 30 Jul 2013 • Jingdong Wang, Jing Wang, Gang Zeng, Zhuowen Tu, Rui Gan, Shipeng Li
The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data.
no code implementations • 30 Jul 2013 • Jingdong Wang, Hao Xu, Xian-Sheng Hua, Shipeng Li
We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images.
no code implementations • CVPR 2013 • Peng Wang, Jingdong Wang, Gang Zeng, Weiwei Xu, Hongbin Zha, Shipeng Li
In visual recognition tasks, the design of low level image feature representation is fundamental.