no code implementations • 17 May 2013 • Kaixu Zhang, Can Wang, Maosong Sun
A binary tree based framework is also designed to overcome the granularity mismatch problem.
2 code implementations • 9 May 2016 • Liqian Ma, Hong Liu, Liang Hu, Can Wang, Qianru Sun
Experimental results on three public datasets and two proposed datasets demonstrate the superiority of the proposed approach, indicating the effectiveness of body structure and orientation information for improving re-identification performance.
no code implementations • 26 Aug 2017 • Kui Zhao, Bangpeng Li, Zilun Peng, Jiajun Bu, Can Wang
Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2. 0 sites.
no code implementations • 26 Aug 2017 • Kui Zhao, Can Wang
To overcome the limitations of existing methods, we propose a novel approach in this paper to learn effective features automatically from the structured data using the Convolutional Neural Network (CNN).
no code implementations • 26 Aug 2017 • Kui Zhao, Xia Hu, Jiajun Bu, Can Wang
In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.
no code implementations • 16 Apr 2018 • Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, Can Wang
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
3 code implementations • CVPR 2019 • Jiefeng Li, Can Wang, Hao Zhu, Yihuan Mao, Hao-Shu Fang, Cewu Lu
In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms.
Ranked #6 on Multi-Person Pose Estimation on OCHuman
2 code implementations • 31 Jan 2019 • Sheng Zhou, Jiajun Bu, Xin Wang, Jia-Wei Chen, Can Wang
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
no code implementations • 22 May 2019 • Tianye Zhang, Haozhe Feng, Zexian Chen, Can Wang, Yanhao Huang, Yong Tang, Wei Chen
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid.
3 code implementations • 14 Nov 2019 • Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on Graph Classification on PROTEINS
2 code implementations • 1 Dec 2019 • Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, Chun Chen
The second-level distillation is performed to transfer the knowledge in the ensemble of auxiliary peers further to the group leader, i. e., the model used for inference.
no code implementations • 4 Mar 2020 • Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen
A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence.
3 code implementations • NeurIPS 2020 • Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #2 on Traffic Prediction on BJTaxi
no code implementations • 12 Jul 2020 • Guang Liang, Shangfei Wang, Can Wang
The first aims to learn pose- and expression-related feature representations in the source domain and adapt both feature distributions to that of the target domain by imposing adversarial learning.
1 code implementation • 14 Jul 2020 • Zhe Liu, Lina Yao, Lei Bai, Xianzhi Wang, Can Wang
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.
2 code implementations • ECCV 2020 • Sheng Jin, Lumin Xu, Jin Xu, Can Wang, Wentao Liu, Chen Qian, Wanli Ouyang, Ping Luo
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
Ranked #8 on 2D Human Pose Estimation on COCO-WholeBody
no code implementations • ECCV 2020 • Jiefeng Li, Can Wang, Wentao Liu, Chen Qian, Cewu Lu
The HMOR encodes interaction information as the ordinal relations of depths and angles hierarchically, which captures the body-part and joint level semantic and maintains global consistency at the same time.
3D Multi-Person Pose Estimation (absolute) 3D Multi-Person Pose Estimation (root-relative) +2
no code implementations • 11 Sep 2020 • Ye Tao, Can Wang, Lina Yao, Weimin Li, Yonghong Yu
Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.
no code implementations • 16 Nov 2020 • Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
1 code implementation • 16 Nov 2020 • Can Wang, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen
However, the social network information may not be available in many recommender systems, which hinders application of SamWalker.
2 code implementations • 6 Dec 2020 • Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Yan Feng, Chun Chen
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model.
1 code implementation • 22 Apr 2021 • Can Wang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao
Face image manipulation via three-dimensional guidance has been widely applied in various interactive scenarios due to its semantically-meaningful understanding and user-friendly controllability.
3 code implementations • ICCV 2021 • Jiefeng Li, Siyuan Bian, Ailing Zeng, Can Wang, Bo Pang, Wentao Liu, Cewu Lu
In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution.
Ranked #59 on 3D Human Pose Estimation on Human3.6M
no code implementations • 4 Aug 2021 • Fangzhou Han, Can Wang, Hao Du, Jing Liao
To address this, we present a novel deep learning framework for portrait lighting enhancement based on 3D facial guidance.
1 code implementation • ICCV 2021 • Sheng Zhou, Yucheng Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, Jiajun Bu
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.
no code implementations • 14 Sep 2021 • Defang Chen, Can Wang, Yan Feng, Chun Chen
Knowledge distillation is a generalized logits matching technique for model compression.
2 code implementations • CVPR 2022 • Can Wang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao
Furthermore, we propose an inverse optimization method that accurately projects an input image to the latent codes for manipulation to enable editing on real images.
no code implementations • 28 Dec 2021 • Can Wang, Zhe Wang, Defang Chen, Sheng Zhou, Yan Feng, Chun Chen
However, its effect on graph neural networks is less than satisfactory since the graph topology and node attributes are likely to change in a dynamic way and in this case a static teacher model is insufficient in guiding student training.
1 code implementation • 30 Dec 2021 • Hailin Zhang, Defang Chen, Can Wang
Knowledge distillation is initially introduced to utilize additional supervision from a single teacher model for the student model training.
no code implementations • ICLR 2022 • Can Wang, Sheng Jin, Yingda Guan, Wentao Liu, Chen Qian, Ping Luo, Wanli Ouyang
PL approaches apply pseudo-labels to unlabeled data, and then train the model with a combination of the labeled and pseudo-labeled data iteratively.
no code implementations • 16 Feb 2022 • Shiya Luo, Defang Chen, Can Wang
Knowledge distillation aims to enhance the performance of a lightweight student model by exploiting the knowledge from a pre-trained cumbersome teacher model.
1 code implementation • CVPR 2022 • Defang Chen, Jian-Ping Mei, Hailin Zhang, Can Wang, Yan Feng, Chun Chen
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance.
Ranked #3 on Knowledge Distillation on CIFAR-100
1 code implementation • 5 May 2022 • Jiongyu Guo, Defang Chen, Can Wang
Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its performance.
no code implementations • 7 Jun 2022 • Zhehui Zhou, Defang Chen, Can Wang, Yan Feng, Chun Chen
Iteratively incorporating and accumulating iteration-based semantic information enables the low-dimensional model to be more expressive for better link prediction in KGs.
1 code implementation • 11 Aug 2022 • Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, Jing Liao
Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs.
no code implementations • 25 Oct 2022 • Jiongyu Guo, Defang Chen, Can Wang
Alignahead++ transfers structure and feature information in a student layer to the previous layer of another simultaneously trained student model in an alternating training procedure.
1 code implementation • 22 Nov 2022 • Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, Chun Chen
Instead of aligning output images, we distill teacher's sharpened feature distribution into the student with a dataset-independent classifier, making the student focus on those important features to improve performance.
1 code implementation • 15 Dec 2022 • Can Wang, Ruixiang Jiang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao
As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images.
no code implementations • 28 Dec 2022 • Qihao Shi, Bingyang Fu, Can Wang, Jiawei Chen, Sheng Zhou, Yan Feng, Chun Chen
The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology.
no code implementations • 19 Feb 2023 • Qihao Shi, Wenjie Tian, Wujian Yang, Mengqi Xue, Can Wang, Minghui Wu
In this paper, we propose a new influence spread model, namely, Complementary\&Competitive Independent Cascade (C$^2$IC) model.
1 code implementation • ICCV 2023 • Ruixiang Jiang, Can Wang, Jingbo Zhang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao
Neural implicit fields are powerful for representing 3D scenes and generating high-quality novel views, but it remains challenging to use such implicit representations for creating a 3D human avatar with a specific identity and artistic style that can be easily animated.
1 code implementation • IEEE Transactions on Multimedia 2023 • Jinfu Liu, Xinshun Wang, Can Wang, Yuan Gao, Mengyuan Liu
Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints.
1 code implementation • 19 May 2023 • Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, Jing Liao
Extensive experiments demonstrate that our Text2NeRF outperforms existing methods in producing photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of natural language prompts.
no code implementations • 31 May 2023 • Defang Chen, Zhenyu Zhou, Jian-Ping Mei, Chunhua Shen, Chun Chen, Can Wang
Recent years have witnessed significant progress in developing effective training and fast sampling techniques for diffusion models.
1 code implementation • 11 Jun 2023 • Hailin Zhang, Defang Chen, Can Wang
Multi-Teacher knowledge distillation provides students with additional supervision from multiple pre-trained teachers with diverse information sources.
1 code implementation • NeurIPS 2023 • Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, Can Wang
Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.
1 code implementation • 10 Jul 2023 • Shiya Luo, Defang Chen, Can Wang
Existing works generally synthesize data from the pre-trained teacher model to replace the original training data for student learning.
1 code implementation • 13 Aug 2023 • Zijie Song, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data.
1 code implementation • 25 Sep 2023 • Yang Liu, Chen Chen, Can Wang, Xulin King, Mengyuan Liu
The proposed method decouples functions between the decoder and the encoder by introducing a mask regressor, which predicts the masked patch representation from the visible patch representation encoded by the encoder and the decoder reconstructs the target from the predicted masked patch representation.
Ranked #3 on Few-Shot 3D Point Cloud Classification on ModelNet40 10-way (20-shot) (using extra training data)
Few-Shot 3D Point Cloud Classification Representation Learning +1
no code implementations • 29 Nov 2023 • Xinshun Wang, Wanying Zhang, Can Wang, Yuan Gao, Mengyuan Liu
Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task.
2 code implementations • 30 Nov 2023 • Zhenyu Zhou, Defang Chen, Can Wang, Chun Chen
Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs), with the aim of obtaining an accurate solution with as few number of function evaluations (NFE) as possible.
no code implementations • 4 Dec 2023 • Can Wang, Mingming He, Menglei Chai, Dongdong Chen, Jing Liao
We first introduce a differentiable method using marching tetrahedra for polygonal mesh extraction from the neural implicit field and then design a differentiable color extractor to assign colors obtained from the volume renderings to this extracted mesh.
no code implementations • 22 Dec 2023 • Wanchao Su, Can Wang, Chen Liu, Hangzhou Han, Hongbo Fu, Jing Liao
To address such issues, we present StyleRetoucher, a novel automatic portrait image retouching framework, leveraging StyleGAN's generation and generalization ability to improve an input portrait image's skin condition while preserving its facial details.
1 code implementation • 11 Jan 2024 • Wujie Sun, Defang Chen, Jiawei Chen, Yan Feng, Chun Chen, Can Wang
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead.
1 code implementation • 20 Feb 2024 • Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
no code implementations • 18 Apr 2024 • Siyi Lin, Chongming Gao, Jiawei Chen, Sheng Zhou, Binbin Hu, Can Wang
Our comprehensive theoretical and empirical investigations lead to two core insights: 1) Item popularity is memorized in the principal singular vector of the score matrix predicted by the recommendation model; 2) The dimension collapse phenomenon amplifies the impact of principal singular vector on model predictions, intensifying the popularity bias.
no code implementations • 18 Apr 2024 • Sirui Chen, Jiawei Chen, Sheng Zhou, Bohao Wang, Shen Han, Chanfei Su, Yuqing Yuan, Can Wang
Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences.