no code implementations • 3 Apr 2024 • Simiao Li, Yun Zhang, Wei Li, Hanting Chen, Wenjia Wang, BingYi Jing, Shaohui Lin, Jie Hu
Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model.
no code implementations • 28 Mar 2024 • Kexin Shi, Jing Zhang, Linjiajie Fang, Wenjia Wang, BingYi Jing
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning.
no code implementations • 26 Mar 2024 • Qingping Sun, Yanjun Wang, Ailing Zeng, Wanqi Yin, Chen Wei, Wenjia Wang, Haiyi Mei, Chi Sing Leung, Ziwei Liu, Lei Yang, Zhongang Cai
Expressive human pose and shape estimation (a. k. a.
no code implementations • 17 Mar 2024 • Yongtao Ge, Wenjia Wang, Yongfan Chen, Hao Chen, Chunhua Shen
In this work, we show that synthetic data created by generative models is complementary to computer graphics (CG) rendered data for achieving remarkable generalization performance on diverse real-world scenes for 3D human pose and shape estimation (HPS).
no code implementations • 26 Feb 2024 • Xuantong Liu, Tianyang Hu, Wenjia Wang, Kenji Kawaguchi, Yuan YAO
In this work, we aim to address this alignment challenge for conditional generation tasks.
no code implementations • 23 Feb 2024 • Jiajun Ma, Shuchen Xue, Tianyang Hu, Wenjia Wang, Zhaoqiang Liu, Zhenguo Li, Zhi-Ming Ma, Kenji Kawaguchi
Surprisingly, the improvement persists when we increase the number of sampling steps and can even surpass the best result from EDM-2 (1. 58) with only 39 NFEs (1. 57).
no code implementations • 8 Feb 2024 • Jun Wang, Haoxuan Li, Chi Zhang, Dongxu Liang, Enyun Yu, Wenwu Ou, Wenjia Wang
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction.
3 code implementations • 6 Feb 2024 • Jun Wang, Wenjie Du, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, Qingsong Wen
In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods.
no code implementations • 4 Dec 2023 • Wenyang Zhou, Zhiyang Dou, Zeyu Cao, Zhouyingcheng Liao, Jingbo Wang, Wenjia Wang, YuAn Liu, Taku Komura, Wenping Wang, Lingjie Liu
We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation.
no code implementations • 3 Dec 2023 • Wenjia Wang, Xiaowei Zhang, Lu Zou
We establish new upper bounds on both the simple and cumulative regret of GP-UCB when the objective function to optimize admits certain smoothness property.
no code implementations • 5 Nov 2023 • Xuqian Ren, Wenjia Wang, Dingding Cai, Tuuli Tuominen, Juho Kannala, Esa Rahtu
Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e. g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism.
1 code implementation • 17 Oct 2023 • Jiajun Ma, Tianyang Hu, Wenjia Wang, Jiacheng Sun
Guidance in conditional diffusion generation is of great importance for sample quality and controllability.
Ranked #1 on Conditional Image Generation on ImageNet 128x128
1 code implementation • 25 Sep 2023 • Yun Zhang, Wei Li, Simiao Li, Hanting Chen, Zhijun Tu, Wenjia Wang, BingYi Jing, Shaohui Lin, Jie Hu
Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models.
Ranked #22 on Image Super-Resolution on Urban100 - 4x upscaling
no code implementations • 20 Jul 2023 • Yanjun Wang, Qingping Sun, Wenjia Wang, Jun Ling, Zhongang Cai, Rong Xie, Li Song
Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module.
no code implementations • 5 May 2023 • Liang Ding, Tianyang Hu, Jiahang Jiang, Donghao Li, Wenjia Wang, Yuan YAO
In this paper, we aim to bridge this gap by presenting a framework for random smoothing regularization that can adaptively and effectively learn a wide range of ground truth functions belonging to the classical Sobolev spaces.
1 code implementation • ICCV 2023 • Wenjia Wang, Yongtao Ge, Haiyi Mei, Zhongang Cai, Qingping Sun, Yanjun Wang, Chunhua Shen, Lei Yang, Taku Komura
As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh reconstruction (3DHMR) methods either use a constant large focal length or estimate one based on the background environment context, which can not tackle the problem of the torso, limb, hand or face distortion caused by perspective camera projection when the camera is close to the human body.
Ranked #5 on 3D Human Pose Estimation on 3DPW
no code implementations • 28 Jan 2023 • Jiajun Ma, Tianyang Hu, Wenjia Wang
In this work, we systematically investigate the role of the projection head in SSL.
1 code implementation • NeurIPS 2023 • Jing Zhang, Chi Zhang, Wenjia Wang, Bing-Yi Jing
Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points.
no code implementations • 7 Dec 2022 • Xuanyu Shi, Shiyao Xie, Wenjia Wang, Ting Chen, Jian Du
Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken.
no code implementations • 25 Nov 2022 • Kexin Shi, Yun Zhang, BingYi Jing, Wenjia Wang
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs).
2 code implementations • 30 May 2022 • Tianyang Hu, Zhili Liu, Fengwei Zhou, Wenjia Wang, Weiran Huang
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data.
no code implementations • 28 Apr 2022 • Zhongang Cai, Daxuan Ren, Ailing Zeng, Zhengyu Lin, Tao Yu, Wenjia Wang, Xiangyu Fan, Yang Gao, Yifan Yu, Liang Pan, Fangzhou Hong, Mingyuan Zhang, Chen Change Loy, Lei Yang, Ziwei Liu
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
no code implementations • 16 Mar 2022 • Chunmeng Liu, Enze Xie, Wenjia Wang, Wenhai Wang, Guangyao Li, Ping Luo
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insufficient to capture global context information, leading to sub-optimal results.
no code implementations • 10 Jan 2022 • Yangyang Wu, Jun Wang, Xiaoye Miao, Wenjia Wang, Jianwei Yin
DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model.
no code implementations • 9 Jan 2022 • Wenjia Wang, Yanyuan Wang, Xiaowei Zhang
Nested simulation concerns estimating functionals of a conditional expectation via simulation.
no code implementations • 7 Dec 2021 • Tianyang Hu, Jun Wang, Wenjia Wang, Zhenguo Li
Comparing to cross-entropy, square loss has comparable generalization error but noticeable advantages in robustness and model calibration.
2 code implementations • 21 Jan 2021 • Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, Ping Luo
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.
Ranked #3 on Semantic Segmentation on Trans10K
no code implementations • 21 Nov 2020 • Cheolhei Lee, Jianguo Wu, Wenjia Wang, Xiaowei Yue
Developing machine learning enabled smart manufacturing is promising for composite structures assembly process.
no code implementations • 6 Jul 2020 • Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng
Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data.
no code implementations • 5 Jun 2020 • Liang Ding, Lu Zou, Wenjia Wang, Shahin Shahrampour, Rui Tuo
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization.
4 code implementations • ECCV 2020 • Wenjia Wang, Enze Xie, Xuebo Liu, Wenhai Wang, Ding Liang, Chunhua Shen, Xiang Bai
For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN.
1 code implementation • ECCV 2020 • Enze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding, Chunhua Shen, Ping Luo
To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.
Ranked #4 on Semantic Segmentation on Trans10K
no code implementations • 4 Feb 2020 • Rui Tuo, Wenjia Wang
Bayesian optimization is a class of global optimization techniques.
1 code implementation • 4 Nov 2019 • Jie Zhao, Lei Dai, Mo Zhang, Fei Yu, Meng Li, Hongfeng Li, Wenjia Wang, Li Zhang
The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.
1 code implementation • 16 Sep 2019 • Wenjia Wang, Enze Xie, Peize Sun, Wenhai Wang, Lixun Tian, Chunhua Shen, Ping Luo
Nonetheless, most of the previous methods may not work well in recognizing text with low resolution which is often seen in natural scene images.
6 code implementations • ICCV 2019 • Wenhai Wang, Enze Xie, Xiaoge Song, Yuhang Zang, Wenjia Wang, Tong Lu, Gang Yu, Chunhua Shen
Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.
Ranked #8 on Scene Text Detection on SCUT-CTW1500
2 code implementations • 19 Apr 2019 • Wenjia Wang, Junxuan Chen, Jie Zhao, Ying Chi, Xuansong Xie, Li Zhang, Xian-Sheng Hua
The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0. 947 $\pm$ 0. 044.
3 code implementations • 17 Nov 2017 • Jiahong Wu, He Zheng, Bo Zhao, Yixin Li, Baoming Yan, Rui Liang, Wenjia Wang, Shipei Zhou, Guosen Lin, Yanwei Fu, Yizhou Wang, Yonggang Wang
Significant progress has been achieved in Computer Vision by leveraging large-scale image datasets.