Structural, Syntactic, and Statistical Pattern Recognition
Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006. Proceedings
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Chapter and Conference Paper
3D-Aware Indoor Scene Synthesis with Depth Priors
Despite the recent advancement of Generative Adversarial Networks (GANs) in learning 3D-aware image synthesis from 2D data, existing methods fail to model indoor scenes due to the large diversity of room layou...
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Chapter and Conference Paper
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
Contemporary deep-learning object detection methods for autonomous driving usually presume fixed categories of common traffic participants, such as pedestrians and cars. Most existing detectors are unable to d...
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Chapter and Conference Paper
Learning Accurate Objectness Instance Segmentation from Photorealistic Rendering for Robotic Manipulation
Recent progress in computer vision has been driven by high-capacity deep convolutional neural network (CNN) models trained on generic large datasets. However, creating large datasets with dense pixel-level lab...
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Article
Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction
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 researc...
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Chapter and Conference Paper
Semi-semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments
This paper presents a novel method to reduce the scale drift for indoor monocular simultaneous localization and map** (SLAM). We leverage the prior knowledge that in the indoor environment, the line segments...
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Chapter and Conference Paper
The Visual Object Tracking VOT2016 Challenge Results
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a l...
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Article
Active hashing and its application to image and text retrieval
In recent years, hashing-based methods for large-scale similarity search have sparked considerable research interests in the data mining and machine learning communities. While unsupervised hashing-based metho...
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Chapter and Conference Paper
Multi-Task Boosting by Exploiting Task Relationships
Multi-task learning aims at improving the performance of one learning task with the help of other related tasks. It is particularly useful when each task has very limited labeled data. A central issue in multi...
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Chapter and Conference Paper
A Probabilistic Approach to Robust Matrix Factorization
Matrix factorization underlies a large variety of computer vision applications. It is a particularly challenging problem for large-scale applications and when there exist outliers and missing data. In this pap...
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Chapter and Conference Paper
Discriminative Experimental Design
Since labeling data is often both laborious and costly, the labeled data available in many applications is rather limited. Active learning is a learning approach which actively selects unlabeled data points to...
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Chapter and Conference Paper
Learning Inverse Dynamics by Gaussian process Begrression under the Multi-Task Learning Framework
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Chapter and Conference Paper
Semi-Supervised Multi-Task Regression
Labeled data are needed for many machine learning applications but the amount available in some applications is scarce. Semi-supervised learning and multi-task learning are two of the approaches that have been...
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Chapter and Conference Paper
Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-supervised Extension
Linear discriminant analysis (LDA) is a commonly used method for dimensionality reduction. Despite its successes, it has limitations under some situations, including the small sample size problem...
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Chapter and Conference Paper
Learning Two-View Stereo Matching
We propose a graph-based semi-supervised symmetric matching framework that performs dense matching between two uncalibrated wide-baseline images by exploiting the results of sparse matching as labeled data. Ou...
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Chapter and Conference Paper
Structuring Visual Words in 3D for Arbitrary-View Object Localization
We propose a novel and efficient method for generic arbitrary-view object class detection and localization. In contrast to existing single-view and multi-view methods using complicated mechanisms for relating ...
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Chapter and Conference Paper
Semi-supervised Discriminant Analysis Via CCCP
Linear discriminant analysis (LDA) is commonly used for dimensionality reduction. In real-world applications where labeled data are scarce, LDA does not work very well. However, unlabeled data are often availa...
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Article
Surrogate maximization/minimization algorithms and extensions
Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. An SM algorithm aims at turning an oth...
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Article
Throttling spoofed SYN flooding traffic at the source
TCP-based flooding attacks are a common form of Distributed Denial-of-Service (DDoS) attacks which abuse network resources and can bring about serious threats to the Internet. Incorporating IP spoofing makes i...
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Article
Model-based transductive learning of the kernel matrix
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construct a hierarchical generative m...
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Book and Conference Proceedings
