111 citations · 309 across the 22 of their papers we have counts for
21 papers · 1 filter
STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization
Yijin Chen, Xun Xu, Yongyi Su +1
Domain adaptation helps generalizing object detection models to target domain data with distribution shift. It is often achieved by adapting with access to the whole target domain…
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Nanqing Liu, Xun Xu, Turgay Celik +2
Object detection in remote sensing images relies on a large amount of labeled data for training. However, the increasing number of new categories and class imbalance make exhaustiv…
Deep Negative Correlation Classification
Le Zhang, Qibin Hou, Yun Liu +4
Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm. Existing deep ensemble methods usually naively train many dif…
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
Yongyi Su, Xun Xu, Kui Jia
Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning. Existing approaches mainly focus on exploiting manifold an…
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
Jinpeng Lin, Zhihao Liang, Shengheng Deng +5
3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the av…
Bootstrap Motion Forecasting With Self-Consistent Constraints
Maosheng Ye, Jiamiao Xu, Xunnong Xu +3
We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints (MISC). The motion forecasting task aims at predicting future trajectories of vehicles…
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
Shengheng Deng, Xun Xu, Chaozheng Wu +2
The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing,…
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Xian Shi, Xun Xu, Ke Chen +3
Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets. Howe…
Learning Category-level Shape Saliency via Deep Implicit Surface Networks
Chaozheng Wu, Lin Sun, Xun Xu +1
This paper is motivated from a fundamental curiosity on what defines a category of object shapes. For example, we may have the common knowledge that a plane has wings, and a chair…
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels
Xun Xu, Gim Hee Lee
Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network des…
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models
Xun Xu, Loong-Fah Cheong, Zhuwen Li
Many real-world video sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography…
C3AE: Exploring the Limits of Compact Model for Age Estimation
Chao Zhang, Shuaicheng Liu, Xun Xu +1
Age estimation is a classic learning problem in computer vision. Many larger and deeper CNNs have been proposed with promising performance, such as AlexNet, VggNet, GoogLeNet and R…
Learning for Multi-Type Subspace Clustering
Xun Xu, Loong-Fah Cheong, Zhuwen Li
Subspace clustering has been extensively studied from the hypothesis-and-test, algebraic, and spectral clustering based perspectives. Most assume that only a single type/class of s…
Robust Video Background Identification by Dominant Rigid Motion Estimation
Kaimo Lin, Nianjuan Jiang, Loong Fah Cheong +2
The ability to identify the static background in videos captured by a moving camera is an important pre-requisite for many video applications (e.g. video stabilization, stitching,…
Learning for Multi-Model and Multi-Type Fitting
Xun Xu, Loong-Fah Cheong, Zhuwen Li
Multi-model fitting has been extensively studied from the random sampling and clustering perspectives. Most assume that only a single type/class of model is present and their gener…
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Chao Zhang, Ce Zhu, Jimin Xiao +2
Image ordinal classification refers to predicting a discrete target value which carries ordering correlation among image categories. The limited size of labeled ordinal data render…
Motion Segmentation by Exploiting Complementary Geometric Models
Xun Xu, Loong-Fah Cheong, Zhuwen Li
Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model…
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation
Xun Xu, Timothy M. Hospedales, Shaogang Gong
Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing the conventional model training requirement of annotated examples for every category. This is achieved by…
Latent Model Ensemble with Auto-localization
Miao Sun, Tony X. Han, Xun Xu +2
Deep Convolutional Neural Networks (CNN) have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene label- ing…
Transductive Zero-Shot Action Recognition by Word-Vector Embedding
Xun Xu, Timothy Hospedales, Shaogang Gong
The number of categories for action recognition is growing rapidly and it has become increasingly hard to label sufficient training data for learning conventional models for all ca…
Semantic Embedding Space for Zero-Shot Action Recognition
Xun Xu, Timothy Hospedales, Shaogang Gong
The number of categories for action recognition is growing rapidly. It is thus becoming increasingly hard to collect sufficient training data to learn conventional models for each…