Search Results for author: Dit-yan Yeung

Found 43 papers, 13 papers with code

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

no code implementations27 Aug 2021 Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-yan Yeung

By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training.

Autonomous Driving Image Clustering +2

Stereo Waterdrop Removal with Row-wise Dilated Attention

1 code implementation7 Aug 2021 Zifan Shi, Na Fan, Dit-yan Yeung, Qifeng Chen

Thus, we propose a learning-based model for waterdrop removal with stereo images.

Autonomous Driving

Probing Toxic Content in Large Pre-Trained Language Models

1 code implementation ACL 2021 Nedjma Ousidhoum, Xinran Zhao, Tianqing Fang, Yangqiu Song, Dit-yan Yeung

Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems.

Probing Language Models

Knowledge Query Network: How Knowledge Interacts with Skills

no code implementations3 Aug 2019 Jinseok Lee, Dit-yan Yeung

This involves abstract concepts of students' states of knowledge and the interactions between those states and skills.

Knowledge Tracing

Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation

no code implementations9 Jun 2019 Ting Sun, Yuxiang Sun, Ming Liu, Dit-yan Yeung

Moving objects can greatly jeopardize the performance of a visual simultaneous localization and mapping (vSLAM) system which relies on the static-world assumption.

Simultaneous Localization and Mapping Weakly-Supervised Semantic Segmentation

Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses

no code implementations12 Dec 2018 Mucong Ding, Kai Yang, Dit-yan Yeung, Ting-Chuen Pong

A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand.

Semi-Semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments

no code implementations5 Nov 2018 Ting Sun, Dezhen Song, Dit-yan Yeung, Ming Liu

In the back end, we optimize the map imposing the constraint that the line segments of the same cluster should be the same.

Simultaneous Localization and Mapping

Point-cloud-based place recognition using CNN feature extraction

no code implementations23 Oct 2018 Ting Sun, Ming Liu, Haoyang Ye, Dit-yan Yeung

This paper proposes a novel point-cloud-based place recognition system that adopts a deep learning approach for feature extraction.

Machine Learning for Spatiotemporal Sequence Forecasting: A Survey

no code implementations21 Aug 2018 Xingjian Shi, Dit-yan Yeung

Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem.

Trajectory Forecasting

Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization

2 code implementations6 Jun 2018 Chun-kit Yeung, Dit-yan Yeung

In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods.

Knowledge Tracing

Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction

1 code implementation6 Jun 2018 Chun-kit Yeung, Zizheng Lin, Kai Yang, Dit-yan Yeung

The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community.

Job Prediction Knowledge Tracing

Robust path-based spectral clustering

no code implementations1 Jan 2018 Hong Chang, Dit-yan Yeung

In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based similarity measure for spectral clustering under both unsupervised and semi-supervised settings.

Semantic Segmentation

Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

no code implementations24 Sep 2017 Siyi Li, Tianbo Liu, Chi Zhang, Dit-yan Yeung, Shaojie Shen

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process.

Temporal Dynamic Graph LSTM for Action-driven Video Object Detection

no code implementations ICCV 2017 Yuan Yuan, Xiaodan Liang, Xiaolong Wang, Dit-yan Yeung, Abhinav Gupta

A common issue, however, is that objects of interest that are not involved in human actions are often absent in global action descriptions known as "missing label".

Object Recognition Video Object Detection +1

Spatiotemporal Modeling for Crowd Counting in Videos

no code implementations ICCV 2017 Feng Xiong, Xingjian Shi, Dit-yan Yeung

To exploit the otherwise very useful temporal information in video sequences, we propose a variant of a recent deep learning model called convolutional LSTM (ConvLSTM) for crowd counting.

Crowd Counting Transfer Learning

Fine-Grained Categorization via CNN-Based Automatic Extraction and Integration of Object-Level and Part-Level Features

no code implementations22 Jun 2017 Ting Sun, Lin Sun, Dit-yan Yeung

Fine-grained categorization can benefit from part-based features which reveal subtle visual differences between object categories.

ZM-Net: Real-time Zero-shot Image Manipulation Network

no code implementations21 Mar 2017 Hao Wang, Xiaodan Liang, Hao Zhang, Dit-yan Yeung, Eric P. Xing

We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones).

Colorization Image Manipulation +1

Dynamic Key-Value Memory Networks for Knowledge Tracing

no code implementations24 Nov 2016 Jiani Zhang, Xingjian Shi, Irwin King, Dit-yan Yeung

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities.

Knowledge Tracing

Natural-Parameter Networks: A Class of Probabilistic Neural Networks

1 code implementation NeurIPS 2016 Hao Wang, Xingjian Shi, Dit-yan Yeung

Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models.

Decision Making Under Uncertainty Link Prediction

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

no code implementations NeurIPS 2016 Hao Wang, Xingjian Shi, Dit-yan Yeung

To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting.

Denoising Recommendation Systems

Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis

no code implementations17 Sep 2016 Zhourong Chen, Nevin L. Zhang, Dit-yan Yeung, Peixian Chen

We are interested in exploring the possibility and benefits of structure learning for deep models.

Towards Bayesian Deep Learning: A Framework and Some Existing Methods

no code implementations24 Aug 2016 Hao Wang, Dit-yan Yeung

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence.

Object Recognition Recommendation Systems +1

A Survey on Bayesian Deep Learning

1 code implementation6 Apr 2016 Hao Wang, Dit-yan Yeung

The past decade has seen major advances in many perception tasks such as visual object recognition and speech recognition using deep learning models.

Object Recognition Recommendation Systems +2

Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks

no code implementations ICCV 2015 Lin Sun, Kui Jia, Dit-yan Yeung, Bertram E. Shi

Human actions in video sequences are three-dimensional (3D) spatio-temporal signals characterizing both the visual appearance and motion dynamics of the involved humans and objects.

Action Recognition Image Classification

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

7 code implementations NeurIPS 2015 Xingjian Shi, Zhourong Chen, Hao Wang, Dit-yan Yeung, Wai-kin Wong, Wang-chun Woo

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.

Weather Forecasting

DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting

no code implementations CVPR 2015 Chuang Gan, Naiyan Wang, Yi Yang, Dit-yan Yeung, Alex G. Hauptmann

Taking key frames of videos as input, we first detect the event of interest at the video level by aggregating the CNN features of the key frames.

Action Recognition Event Detection +1

Bayesian Adaptive Matrix Factorization With Automatic Model Selection

no code implementations CVPR 2015 Peixian Chen, Naiyan Wang, Nevin L. Zhang, Dit-yan Yeung

Low-rank matrix factorization has long been recognized as a fundamental problem in many computer vision applications.

Model Selection

Understanding and Diagnosing Visual Tracking Systems

no code implementations ICCV 2015 Naiyan Wang, Jianping Shi, Dit-yan Yeung, Jiaya Jia

Surprisingly, our findings are discrepant with some common beliefs in the visual tracking research community.

Visual Tracking

Transferring Rich Feature Hierarchies for Robust Visual Tracking

no code implementations19 Jan 2015 Naiyan Wang, Siyi Li, Abhinav Gupta, Dit-yan Yeung

To fit the characteristics of object tracking, we first pre-train the CNN to recognize what is an object, and then propose to generate a probability map instead of producing a simple class label.

Image Classification Object Detection +2

Collaborative Deep Learning for Recommender Systems

1 code implementation10 Sep 2014 Hao Wang, Naiyan Wang, Dit-yan Yeung

(CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix.

Recommendation Systems Representation Learning

Learning a Deep Compact Image Representation for Visual Tracking

no code implementations NeurIPS 2013 Naiyan Wang, Dit-yan Yeung

In this paper, we study the challenging problem of tracking the trajectory of a moving object in a video with possibly very complex background.

Denoising General Classification +2

Co-Regularized Hashing for Multimodal Data

no code implementations NeurIPS 2012 Yi Zhen, Dit-yan Yeung

Hashing-based methods provide a very promising approach to large-scale similarity search.

Worst-Case Linear Discriminant Analysis

no code implementations NeurIPS 2010 Yu Zhang, Dit-yan Yeung

In this paper, we first analyze the scatter measures used in the conventional linear discriminant analysis~(LDA) model and note that the formulation is based on the average-case view.

Dimensionality Reduction Metric Learning

Probabilistic Multi-Task Feature Selection

no code implementations NeurIPS 2010 Yu Zhang, Dit-yan Yeung, Qian Xu

In this paper, we unify the $l_{1, 2}$ and $l_{1,\infty}$ norms by considering a family of $l_{1, q}$ norms for $1 < q\le\infty$ and study the problem of determining the most appropriate sparsity enforcing norm to use in the context of multi-task feature selection.

Feature Selection Multi-Task Learning

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice

no code implementations NeurIPS 2008 Zhihua Zhang, Michael. I. Jordan, Dit-yan Yeung

The duality between regularization and prior leads to interpreting regularization methods in terms of maximum a posteriori estimation and has motivated Bayesian interpretations of kernel methods.

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