Search Results for author: Kun Zhao

Found 30 papers, 5 papers with code

DIODE: Dilatable Incremental Object Detection

no code implementations12 Aug 2021 Can Peng, Kun Zhao, Sam Maksoud, Tianren Wang, Brian C. Lovell

In this paper, we aim to alleviate this performance decay on multi-step incremental detection tasks by proposing a dilatable incremental object detector (DIODE).

Incremental Learning Object Detection

Cascaded Residual Density Network for Crowd Counting

no code implementations29 Jul 2021 Kun Zhao, Luchuan Song, Bin Liu, Qi Chu, Nenghai Yu

Crowd counting is a challenging task due to the issues such as scale variation and perspective variation in real crowd scenes.

Crowd Counting

Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph

no code implementations30 Apr 2021 Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, Hui Chen, Yichi Zhang, Peng He, Huan Chen, Kun Zhao, Huajun Chen

We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.

Click-Through Rate Prediction Knowledge Graph Embedding +1

Scalable Bayesian Deep Learning with Kernel Seed Networks

no code implementations19 Apr 2021 Sam Maksoud, Kun Zhao, Can Peng, Brian C. Lovell

To address this problem we present a method for performing BDL, namely Kernel Seed Networks (KSN), which does not require a 2-fold increase in the number of parameters.

Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network

no code implementations26 Feb 2021 Chen Li, JinZhe Jiang, YaQian Zhao, RenGang Li, EnDong Wang, Xin Zhang, Kun Zhao

Decision of transfer layers and trainable layers is a major task for design of the transfer convolutional neural networks (CNN).

Hyperparameter Optimization

Polynomial Trajectory Predictions for Improved Learning Performance

no code implementations29 Jan 2021 Ido Freeman, Kun Zhao, Anton Kummert

The rising demand for Active Safety systems in automotive applications stresses the need for a reliable short to mid-term trajectory prediction.

Trajectory Prediction

Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform

2 code implementations20 Oct 2020 Zhiping Jiang, Tom H. Luan, Han Hao, Jing Wang, Xincheng Ren, Kun Zhao, Wei Xi, Yueshen Xu, Rui Li

Three barriers always hamper the research: unknown baseband design and its influence, inadequate hardware, and the lack of versatile and flexible measurement software.

Hardware Architecture

Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected Buildings

1 code implementation3 Oct 2020 Kun Zhao, Yongkun Liu, Siyuan Hao, Shaoxing Lu, Hongbin Liu, Lijian Zhou

Instead of using visual features of the whole image directly as common image-level models based on convolutional neural networks (CNNs) do, the proposed framework firstly obtains the bounding boxes of buildings in street view images from a detector.

General Classification Image Classification

SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification

1 code implementation CVPR 2020 Sam Maksoud, Kun Zhao, Peter Hobson, Anthony Jennings, Brian Lovell

The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems.

Classification General Classification +2

Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN

1 code implementation9 Mar 2020 Can Peng, Kun Zhao, Brian C. Lovell

To address this problem, incremental learning methods have been explored which preserve the old knowledge of deep learning models.

Incremental Learning Knowledge Distillation +1

To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?

no code implementations22 Sep 2019 Can Peng, Kun Zhao, Arnold Wiliem, Teng Zhang, Peter Hobson, Anthony Jennings, Brian C. Lovell

Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a $40\times$ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task.

Image Compression

Visual analytics for team-based invasion sports with significant events and Markov reward process

no code implementations2 Jul 2019 Kun Zhao, Takayuki Osogami, Tetsuro Morimura

To solve this problem, we consider a whole match as a Markov chain of significant events, so that event values can be estimated with a continuous parameter space by solving the Markov chain with a machine learning model.

Deep Instance-Level Hard Negative Mining Model for Histopathology Images

no code implementations24 Jun 2019 Meng Li, Lin Wu, Arnold Wiliem, Kun Zhao, Teng Zhang, Brian C. Lovell

Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i. e, patches) and the task is to predict a single class label to the WSI.

General Classification Multiple Instance Learning

CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

no code implementations24 Jun 2019 Sam Maksoud, Arnold Wiliem, Kun Zhao, Teng Zhang, Lin Wu, Brian C. Lovell

This is because the system can ignore the attention mechanism by assigning equal weights for all members.

AliGraph: A Comprehensive Graph Neural Network Platform

no code implementations23 Feb 2019 Rong Zhu, Kun Zhao, Hongxia Yang, Wei. Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou

An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements.

Distributed, Parallel, and Cluster Computing

Convex Class Model on Symmetric Positive Definite Manifolds

no code implementations14 Jun 2018 Kun Zhao, Arnold Wiliem, Shaokang Chen, Brian C. Lovell

Our proposed framework, named Manifold Convex Class Model, represents each class on SPD manifolds using a convex model, and classification can be performed by computing distances to the convex models.

Classification General Classification +4

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

no code implementations20 Mar 2018 Teng Zhang, Johanna Carvajal, Daniel F. Smith, Kun Zhao, Arnold Wiliem, Peter Hobson, Anthony Jennings, Brian C. Lovell

In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way.

Structured Illumination in Spatial-Orientational Hyperspace

1 code implementation14 Dec 2017 Karl Zhanghao, Xingye Chen, Wenhui Liu, Meiqi Li, Chunyan Shan, Xiao Wang, Kun Zhao, Amit Lai, Hao Xie, Qionghai Dai, Peng Xi

The dipole nature of chromophore is important for both super-resolution microscopy and imaging molecular structure, which is nevertheless neglected in most microscopies, even including structured illumination microscopy (SIM) with polarized excitations.


Topic Modeling the Hàn diăn Ancient Classics

no code implementations2 Feb 2017 Colin Allen, Hongliang Luo, Jaimie Murdock, Jianghuai Pu, XiaoHong Wang, Yanjie Zhai, Kun Zhao

In this paper we describe a collaborative effort between Indiana University and Xi'an Jiaotong University to support exploration and interpretation of a digital corpus of over 18, 000 ancient Chinese documents, which we refer to as the "Handian" ancient classics corpus (H\`an di\u{a}n g\u{u} j\'i, i. e, the "Han canon" or "Chinese classics").

Determining the best attributes for surveillance video keywords generation

no code implementations21 Feb 2016 Liangchen Liu, Arnold Wiliem, Shaokang Chen, Kun Zhao, Brian C. Lovell

In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches. We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets.

Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach

no code implementations18 Sep 2015 Kun Zhao, Azadeh Alavi, Arnold Wiliem, Brian C. Lovell

We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds.

Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets

no code implementations18 Feb 2015 Jiajun Liu, Kun Zhao, Brano Kusy, Ji-Rong Wen, Raja Jurdak

The prediction of periodical time-series remains challenging due to various types of data distortions and misalignments.

Time Series

Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification

no code implementations4 Mar 2014 Azadeh Alavi, Arnold Wiliem, Kun Zhao, Brian C. Lovell, Conrad Sanderson

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance.

Classification Face Recognition +4

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