218 citations · 363 across the 11 of their papers we have counts for
11 papers
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval
Hanxi Li, Jianfei Hu, Bo Li +3
In this work, by re-examining the "matching" nature of Anomaly Detection (AD), we propose a new AD framework that simultaneously enjoys new records of AD accuracy and dramatically…
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
Hanxi Li, Jingqi Wu, Deyin Liu +4
Recent advancements in industrial anomaly detection (AD) have demonstrated that incorporating a small number of anomalous samples during training can significantly enhance accuracy…
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Xinyu Wang, Hanxi Li, Yi Li +2
Visual tracking is a fundamental problem in computer vision. Recently, some deep-learning-based tracking algorithms have been achieving record-breaking performances. However, due t…
Learning Discriminative Representations for Semantic Cross Media Retrieval
Aiwen Jiang, Hanxi Li, Yi Li +1
Heterogeneous gap among different modalities emerges as one of the critical issues in modern AI problems. Unlike traditional uni-modal cases, where raw features are extracted and d…
DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking
Hanxi Li, Yi Li, Fatih Porikli
Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they…
Face Recognition using Optimal Representation Ensemble
Hanxi Li, Chunhua Shen, Yongsheng Gao
Recently, the face recognizers based on linear representations have been shown to deliver state-of-the-art performance. In real-world applications, however, face images usually suf…
Real-time Visual Tracking Using Sparse Representation
Hanxi Li, Chunhua Shen, Qinfeng Shi
The tracker obtains robustness by seeking a sparse representation of the tracking object via norm minimization \cite{Xue_ICCV_09_Track}. However, the high computa…
Totally Corrective Boosting for Regularized Risk Minimization
Chunhua Shen, Hanxi Li, Nick Barnes
Consideration of the primal and dual problems together leads to important new insights into the characteristics of boosting algorithms. In this work, we propose a general framework…
LACBoost and FisherBoost: Optimally Building Cascade Classifiers
Chunhua Shen, Peng Wang, Hanxi Li
Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is…
Boosting through Optimization of Margin Distributions
Chunhua Shen, Hanxi Li
Boosting has attracted much research attention in the past decade. The success of boosting algorithms may be interpreted in terms of the margin theory. Recently it has been shown t…
On the Dual Formulation of Boosting Algorithms
Chunhua Shen, Hanxi Li
We study boosting algorithms from a new perspective. We show that the Lagrange dual problems of AdaBoost, LogitBoost and soft-margin LPBoost with generalized hinge loss are all ent…