activity
20142021
most citedA survey of sparse representation: algorithms and applications

1.1k citations · 2.6k across the 75 of their papers we have counts for

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Showing 2021Show all

20 papers · 1 filter

cs.CV2021★ 4 cited

Keypoint Message Passing for Video-based Person Re-Identification

Di Chen, Andreas Doering, Shanshan Zhang +3

Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Ex…

cs.CV2021★ 9 cited

Fine-Grained Image Analysis with Deep Learning: A Survey

Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha +5

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The…

cs.CV2021

3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds

Le Hui, Lingpeng Wang, Mingmei Cheng +2

3D object tracking in point clouds is still a challenging problem due to the sparsity of LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV track…

cs.CV2021★ 3 cited

Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration

Haobo Jiang, Yaqi Shen, Jin Xie +3

In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsu…

cs.CV2021★ 3 cited

FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation

Guangwei Gao, Guoan Xu, Juncheng Li +3

Real-time semantic segmentation, which can be visually understood as the pixel-level classification task on the input image, currently has broad application prospects, especially i…

cs.CV2021★ 2 cited

Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration

Haobo Jiang, Jin Xie, Jianjun Qian +1

Point cloud registration is a fundamental problem in 3D computer vision. In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which ca…

cs.NI2021★ 3 cited

Going Deeper in Frequency Convolutional Neural Network: A Theoretical Perspective

Xiaohan Zhu, Zhen Cui, Tong Zhang +2

Convolutional neural network (CNN) is one of the most widely-used successful architectures in the era of deep learning. However, the high-computational cost of CNN still hampers mo…

cs.CV2021★ 10 cited

Learning Fair Face Representation With Progressive Cross Transformer

Yong Li, Yufei Sun, Zhen Cui +2

Face recognition (FR) has made extraordinary progress owing to the advancement of deep convolutional neural networks. However, demographic bias among different racial cohorts still…

cs.CV2021★ 24 cited

Graph Jigsaw Learning for Cartoon Face Recognition

Yong Li, Lingjie Lao, Zhen Cui +2

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse a…

cs.CV2021★ 2 cited

Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection

Zhenyu Zhang, Yanhao Ge, Renwang Chen +6

Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on…

eess.IV2021★ 1 cited

DONet: Dual-Octave Network for Fast MR Image Reconstruction

Chun-Mei Feng, Zhanyuan Yang, Huazhu Fu +3

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multi…

cs.CV2021★ 4 cited

SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network

Mingmei Cheng, Le Hui, Jin Xie +1

Point cloud semantic segmentation is a crucial task in 3D scene understanding. Existing methods mainly focus on employing a large number of annotated labels for supervised semantic…

cs.CV2021★ 15 cited

Learning Normal Dynamics in Videos with Meta Prototype Network

Hui Lv, Chen Chen, Zhen Cui +3

Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection. With models trained on the normal data, the reconstructio…

cs.LG2021★ 1 cited

Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks

Haobo Jiang, Jin Xie, Jian Yang

Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning,…

cs.CV2021★ 22 cited

Contrastive Embedding for Generalized Zero-Shot Learning

Zongyan Han, Zhenyong Fu, Shuo Chen +1

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Recent feature gen…

cs.CV2021★ 58 cited

Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

Guangwei Gao, Yi Yu, Jian Yang +2

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face…

cs.LG2021★ 3 cited

Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction

Xuran Xu, Tong Zhang, Chunyan Xu +2

Accurate traffic prediction is crucial to the guidance and management of urban traffics. However, most of the existing traffic prediction models do not consider the computational b…

cs.CV2021★ 9 cited

MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation

Guangwei Gao, Guoan Xu, Yi Yu +3

In recent years, how to strike a good trade-off between accuracy and inference speed has become the core issue for real-time semantic segmentation applications, which plays a vital…

cs.CV2021★ 98 cited

Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition

Le Hui, Mingmei Cheng, Jin Xie +1

Point cloud based retrieval for place recognition is still a challenging problem due to drastic appearance and illumination changes of scenes in changing environments. Existing dee…

cs.LG2021★ 2 cited

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

Qizhou Wang, Bo Han, Tongliang Liu +3

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Dee…