109 citations · 280 across the 28 of their papers we have counts for
25 papers · 1 filter
Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting
Xiaoshuang Chen, Hongtao Lu
Fully-supervised crowd counting is a laborious task due to the large amounts of annotations. Few works focus on weekly-supervised crowd counting, where only the global crowd number…
Student Dangerous Behavior Detection in School
Huayi Zhou, Fei Jiang, Hongtao Lu
Video surveillance systems have been installed to ensure the student safety in schools. However, discovering dangerous behaviors, such as fighting and falling down, usually depends…
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting
Xiaoshuang Chen, Yiru Zhao, Yu Qin +4
Crowd counting aims to learn the crowd density distributions and estimate the number of objects (e.g. persons) in images. The perspective effect, which significantly influences the…
Temporal Continuity Based Unsupervised Learning for Person Re-Identification
Usman Ali, Bayram Bayramli, Hongtao Lu
Person re-identification (re-id) aims to match the same person from images taken across multiple cameras. Most existing person re-id methods generally require a large amount of ide…
Learning Texture Transformer Network for Image Super-Resolution
Fuzhi Yang, Huan Yang, Jianlong Fu +2
We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Recent progress has been made by taking high-resolution images a…
LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation
Weihao Jiang, Zhaozhi Xie, Yaoyi Li +2
The recent development of light-weighted neural networks has promoted the applications of deep learning under resource constraints and mobile applications. Many of these applicatio…
Hierarchical Opacity Propagation for Image Matting
Yaoyi Li, Qingyao Xu, Hongtao Lu
Natural image matting is a fundamental problem in computational photography and computer vision. Deep neural networks have seen the surge of successful methods in natural image mat…
Natural Image Matting via Guided Contextual Attention
Yaoyi Li, Hongtao Lu
Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. Many of these methods can generate visually plausible alpha…
Show, Recall, and Tell: Image Captioning with Recall Mechanism
Li Wang, Zechen Bai, Yonghua Zhang +1
Generating natural and accurate descriptions in image cap-tioning has always been a challenge. In this paper, we pro-pose a novel recall mechanism to imitate the way human con-duct…
Inductive Guided Filter: Real-time Deep Image Matting with Weakly Annotated Masks on Mobile Devices
Yaoyi Li, Jianfu Zhang, Weijie Zhao +1
Recently, significant progress has been achieved in deep image matting. Most of the classical image matting methods are time-consuming and require an ideal trimap which is difficul…
FH-GAN: Face Hallucination and Recognition using Generative Adversarial Network
Bayram Bayramli, Usman Ali, Te Qi +1
There are many factors affecting visual face recognition, such as low resolution images, aging, illumination and pose variance, etc. One of the most important problem is low resolu…
Spatial Shortcut Network for Human Pose Estimation
Te Qi, Bayram Bayramli, Usman Ali +2
Like many computer vision problems, human pose estimation is a challenging problem in that recognizing a body part requires not only information from local area but also from areas…
Rethinking on Multi-Stage Networks for Human Pose Estimation
Wenbo Li, Zhicheng Wang, Binyi Yin +7
Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performa…
Attend More Times for Image Captioning
Jiajun Du, Yu Qin, Hongtao Lu +1
Most attention-based image captioning models attend to the image once per word. However, attending once per word is rigid and is easy to miss some information. Attending more times…
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination
Fei Jiang, Xiao-Yang Liu, Hongtao Lu +1
Sparse coding (SC) is an automatic feature extraction and selection technique that is widely used in unsupervised learning. However, conventional SC vectorizes the input images, wh…
Graph Regularized Tensor Sparse Coding for Image Representation
Fei Jiang, Xiao-Yang Liu, Hongtao Lu +1
Sparse coding (SC) is an unsupervised learning scheme that has received an increasing amount of interests in recent years. However, conventional SC vectorizes the input images, whi…
Accurate Deep Representation Quantization with Gradient Snapping Layer for Similarity Search
Shicong Liu, Hongtao Lu
Recent advance of large scale similarity search involves using deeply learned representations to improve the search accuracy and use vector quantization methods to increase the sea…
Multi-View Constraint Propagation with Consensus Prior Knowledge
Yaoyi Li, Hongtao Lu
In many applications, the pairwise constraint is a kind of weaker supervisory information which can be collected easily. The constraint propagation has been proved to be a success…
Deep CTR Prediction in Display Advertising
Junxuan Chen, Baigui Sun, Hao Li +2
Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction…
Adaptive Affinity Matrix for Unsupervised Metric Learning
Yaoyi Li, Junxuan Chen, Hongtao Lu
Spectral clustering is one of the most popular clustering approaches with the capability to handle some challenging clustering problems. Most spectral clustering methods provide a…
Fast Non-local Stereo Matching based on Hierarchical Disparity Prediction
Xuan Luo, Xuejiao Bai, Shuo Li +2
Stereo matching is the key step in estimating depth from two or more images. Recently, some tree-based non-local stereo matching methods have been proposed, which achieved state-of…
Accelerated Distance Computation with Encoding Tree for High Dimensional Data
Shicong Liu, Junru Shao, Hongtao Lu
We propose a novel distance to calculate distance between high dimensional vector pairs, utilizing vector quantization generated encodings. Vector quantization based methods are su…
Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search
Shicong Liu, Hongtao Lu, Junru Shao
Quantization methods have been introduced to perform large scale approximate nearest search tasks. Residual Vector Quantization (RVQ) is one of the effective quantization methods.…
HCLAE: High Capacity Locally Aggregating Encodings for Approximate Nearest Neighbor Search
Shicong Liu, Junru Shao, Hongtao Lu
Vector quantization-based approaches are successful to solve Approximate Nearest Neighbor (ANN) problems which are critical to many applications. The idea is to generate effective…