1 code implementation • ECCV 2020 • Xiangyu Zhu, Fan Yang, Di Huang, Chang Yu, Hao Wang, Jianzhu Guo, Zhen Lei, Stan Z. Li
However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space.
no code implementations • ICLR 2022 • Di Huang, Rui Zhang, Xing Hu, Xishan Zhang, Pengwei Jin, Nan Li, Zidong Du, Qi Guo, Yunji Chen
In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.
no code implementations • 7 May 2022 • Bing Li, Jiaxin Chen, Dongming Zhang, Xiuguo Bao, Di Huang
To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net).
no code implementations • CVPR 2022 • Jiaxi Wu, Jiaxin Chen, Mengzhe He, Yiru Wang, Bo Li, Bingqi Ma, Weihao Gan, Wei Wu, Yali Wang, Di Huang
Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain.
no code implementations • CVPR 2022 • Jiaxi Wu, Jiaxin Chen, Di Huang
Active learning is a promising alternative to alleviate the issue of high annotation cost in the computer vision tasks by consciously selecting more informative samples to label.
no code implementations • 9 Apr 2022 • Xiangyu Zhu, Chang Yu, Di Huang, Zhen Lei, Hao Wang, Stan Z. Li
3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori.
no code implementations • CVPR 2022 • Yanan Zhang, Jiaxin Chen, Di Huang
In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities with complementary cues for 3D object detection.
1 code implementation • CVPR 2022 • Mingwu Zheng, Hongyu Yang, Di Huang, Liming Chen
Precise representations of 3D faces are beneficial to various computer vision and graphics applications.
1 code implementation • CVPR 2022 • Biwen Lei, Xiefan Guo, Hongyu Yang, Miaomiao Cui, Xuansong Xie, Di Huang
The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL).
no code implementations • 21 Dec 2021 • Zichen Yang, Jie Qin, Di Huang
Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
no code implementations • 20 Dec 2021 • Yecheng Huang, Jiaxin Chen, Di Huang
This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det).
no code implementations • 15 Dec 2021 • Xiangnan Yin, Di Huang, Zehua Fu, Yunhong Wang, Liming Chen
The proposed model consists of a 3D face reconstruction module, a face segmentation module, and an image generation module.
1 code implementation • 27 Oct 2021 • Haoxiang Ma, Hongyu Yang, Di Huang
The recent studies on semantic segmentation are starting to notice the significance of the boundary information, where most approaches see boundaries as the supplement of semantic details.
no code implementations • 26 Aug 2021 • Jingcheng Ni, Jie Qin, Di Huang
Action detection plays an important role in high-level video understanding and media interpretation.
no code implementations • 25 Aug 2021 • Huiqun Wang, Ruijie Yang, Di Huang, Yunhong Wang
Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands of times in GPU-day.
no code implementations • ICCV 2021 • Guangyuan Zhou, Huiqun Wang, Jiaxin Chen, Di Huang
This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN), to simultaneously address the issues above in a unified way.
1 code implementation • ICCV 2021 • Xiefan Guo, Hongyu Yang, Di Huang
Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors.
1 code implementation • 8 Aug 2021 • Di Huang, Jacob Bartel, John Palowitch
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content.
no code implementations • 3 Aug 2021 • Yao Wang, Yangtao Zheng, Boyang Gao, Di Huang
This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net).
no code implementations • 14 Jun 2021 • Xiangnan Yin, Di Huang, Zehua Fu, Yunhong Wang, Liming Chen
Missing textures in the incomplete UV map are further full-filled by the UV generator.
no code implementations • 14 Jun 2021 • Xiangnan Yin, Di Huang, Hongyu Yang, Zehua Fu, Yunhong Wang, Liming Chen
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc.
no code implementations • 5 May 2021 • Qingkai Zhen, Di Huang, Yunhong Wang, Hassen Drira, Boulbaba Ben Amor, Mohamed Daoudi
In this paper, an effective pipeline to automatic 4D Facial Expression Recognition (4D FER) is proposed.
6 code implementations • 7 Mar 2021 • Guodong Wang, Shumin Han, Errui Ding, Di Huang
Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.
Ranked #20 on
Anomaly Detection
on MVTec AD
(using extra training data)
no code implementations • 24 Dec 2020 • Ran Qin, Qingjie Liu, Guangshuai Gao, Di Huang, Yunhong Wang
Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected.
no code implementations • 18 Dec 2020 • Yanan Zhang, Di Huang, Yunhong Wang
LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects.
Ranked #4 on
3D Object Detection
on KITTI Cars Easy val
3 code implementations • ECCV 2020 • Jiaxi Wu, Songtao Liu, Di Huang, Yunhong Wang
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Ranked #10 on
Few-Shot Object Detection
on MS-COCO (30-shot)
no code implementations • ECCV 2020 • Yandong Li, Di Huang, Danfeng Qin, Liqiang Wang, Boqing Gong
They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets.
no code implementations • CVPR 2020 • Yangtao Zheng, Di Huang, Songtao Liu, Yunhong Wang
Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred.
no code implementations • 3 Feb 2020 • Di Huang, Xishan Zhang, Rui Zhang, Tian Zhi, Deyuan He, Jiaming Guo, Chang Liu, Qi Guo, Zidong Du, Shaoli Liu, Tianshi Chen, Yunji Chen
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
2 code implementations • 10 Dec 2019 • Jinjin Zhang, Wei Wang, Di Huang, Qingjie Liu, Yunhong Wang
Deep learning based methods have achieved surprising progress in Scene Text Recognition (STR), one of classic problems in computer vision.
no code implementations • 26 Nov 2019 • Mingda Wu, Di Huang, Yuanfang Guo, Yunhong Wang
Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled.
1 code implementation • 21 Nov 2019 • Songtao Liu, Di Huang, Yunhong Wang
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.
Ranked #119 on
Object Detection
on COCO test-dev
1 code implementation • ICML 2020 • Lu Jiang, Di Huang, Mason Liu, Weilong Yang
Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting.
Ranked #9 on
Image Classification
on WebVision-1000
no code implementations • 1 Nov 2019 • Xishan Zhang, Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo, Yu Kang, Qi Guo, Zidong Du, Yunji Chen
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
no code implementations • 25 Sep 2019 • Lu Jiang, Di Huang, Weilong Yang
Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.
no code implementations • 6 Sep 2019 • Arquimedes Canedo, Palash Goyal, Di Huang, Amit Pandey, Gustavo Quiros
We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators.
1 code implementation • 6 Sep 2019 • Palash Goyal, Di Huang, Sujit Rokka Chhetri, Arquimedes Canedo, Jaya Shree, Evan Patterson
In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.
1 code implementation • 19 Aug 2019 • Palash Goyal, Di Huang, Ankita Goswami, Sujit Rokka Chhetri, Arquimedes Canedo, Emilio Ferrara
We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.
1 code implementation • 28 May 2019 • Weicheng Li, Rui Wang, Zhongzhi Luan, Di Huang, Zidong Du, Yunji Chen, Depei Qian
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications.
no code implementations • CVPR 2019 • Songtao Liu, Di Huang, Yunhong Wang
Pedestrian detection in a crowd is a very challenging issue.
Ranked #9 on
Object Detection
on CrowdHuman (full body)
no code implementations • 10 Jan 2019 • Hongyu Yang, Di Huang, Yunhong Wang, Anil K. Jain
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
1 code implementation • CVPR 2018 • Hongyu Yang, Di Huang, Yunhong Wang, Anil K. Jain
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
no code implementations • 26 Nov 2017 • Qiang Chen, Yunhong Wang, Zheng Liu, Qingjie Liu, Di Huang
In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images.
7 code implementations • ECCV 2018 • Songtao Liu, Di Huang, Yunhong Wang
Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs.
no code implementations • 18 Feb 2017 • Chunlei Li, Guangshuai Gao, Zhoufeng Liu, Di Huang, Sheng Liu, Miao Yu
In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper.
no code implementations • 4 Nov 2015 • Hongyu Yang, Di Huang, Yunhong Wang, Heng Wang, Yuanyan Tang
Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images.