Search Results for author:
Found 11 papers, 3 papers with code
MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data
The proposed multi-scale nested decoder structure allows the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features.
Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests
With each patch and the patch sequence of a STC compared to a visual "word" and "sentence" respectively, we deliberately erase a certain "word" (patch) to yield a VCT.
Multi-view Deep One-class Classification: A Systematic Exploration
Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC.
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE).
Ranked #14 on
Anomaly Detection
on CUHK Avenue
Unsupervised Heterogeneous Coupling Learning for Categorical Representation
This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings.
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images.
Understand Dynamic Regret with Switching Cost for Online Decision Making
We provide a new theoretical analysis framework, which shows an interesting observation, that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO.
Simultaneous Clustering and Optimization for Evolving Datasets
Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently.
Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit
We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment.
Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets
The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results.
Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification
In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement.
