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Articles 4951 - 4980 of 9025
Full-Text Articles in Computer Sciences
Formresnet: Formatted Residual Learning For Image Restoration, Jianbo Jiao, Wei-Chih Tu, Shengfeng He
Formresnet: Formatted Residual Learning For Image Restoration, Jianbo Jiao, Wei-Chih Tu, Shengfeng He
Research Collection School Of Computing and Information Systems
In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering the latent clean image together, from the shared information between the corrupted image and the latent image. In addition, instead of learning the pure difference (corruption), we propose to add a 'residual formatting layer' to format the residual to …
Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang
Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang
Research Collection School Of Computing and Information Systems
We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.
Don’T Bury Your Head In Warnings: A Game-Theoretic Approach For Intelligent Allocation Of Cyber-Security Alerts, Aaron Schlenker, Haifeng Xu, Mina Guirguis, Christopher Kiekintveld, Arunesh Sinha, Milind Tambe, Solomon Sonya, Darryl Balderas, Noah Dunstatter
Don’T Bury Your Head In Warnings: A Game-Theoretic Approach For Intelligent Allocation Of Cyber-Security Alerts, Aaron Schlenker, Haifeng Xu, Mina Guirguis, Christopher Kiekintveld, Arunesh Sinha, Milind Tambe, Solomon Sonya, Darryl Balderas, Noah Dunstatter
Research Collection School Of Computing and Information Systems
In recent years, there have been a number of successful cyber attacks on enterprise networks by malicious actors which have caused severe damage. These networks have Intrusion Detection and Prevention Systems in place to protect them, but they are notorious for producing a high volume of alerts. These alerts must be investigated by cyber analysts to determine whether they are an attack or benign. Unfortunately, there are magnitude more alerts generated than there are cyber analysts to investigate them. This trend is expected to continue into the future creating a need for tools which find optimal assignments of the incoming …
The Simpler The Better: A Unified Approach To Predicting Original Taxi Demands On Large-Scale Online Platforms, Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv
The Simpler The Better: A Unified Approach To Predicting Original Taxi Demands On Large-Scale Online Platforms, Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv
Research Collection School Of Computing and Information Systems
No abstract provided.
Measuring Fine-Grained Metro Interchange Time Via Smartphones, Weixi Gu, Kai Zhang, Zimu Zhou, Ming Jin, Yuxun Zhou, Xi Liu, Costas J. Spanos, Zuo-Jun (Max) Shen, Wei-Hua Lin, Lin Zhang
Measuring Fine-Grained Metro Interchange Time Via Smartphones, Weixi Gu, Kai Zhang, Zimu Zhou, Ming Jin, Yuxun Zhou, Xi Liu, Costas J. Spanos, Zuo-Jun (Max) Shen, Wei-Hua Lin, Lin Zhang
Research Collection School Of Computing and Information Systems
High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agentoperated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a …
Sequence Aware Functional Encryption And Its Application In Searchable Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo, Fuchun Guo, Qiong Huang
Sequence Aware Functional Encryption And Its Application In Searchable Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo, Fuchun Guo, Qiong Huang
Research Collection School Of Computing and Information Systems
As a new broad vision of public-key encryption systems, functional encryption provides a promising solution for many challenging security problems such as expressive access control and searching on encrypted data. In this paper, we present two Sequence Aware Function Encryption (SAFE) schemes. Such a scheme is very useful in many forensics applications where the order (or pattern) of the attributes forms an important characteristic of an attribute sequence. Our first scheme supports the matching of two bit strings, while the second scheme can support the matching of general characters. These two schemes are constructed based on the standard Decision Linear …
Optimal Security Reductions For Unique Signatures: Bypassing Impossibilities With A Counterexample, Fuchun Fuo, Rongmao Chen, Willy Susilo, Jianchang Lai, Guomin Yang, Yi Mu
Optimal Security Reductions For Unique Signatures: Bypassing Impossibilities With A Counterexample, Fuchun Fuo, Rongmao Chen, Willy Susilo, Jianchang Lai, Guomin Yang, Yi Mu
Research Collection School Of Computing and Information Systems
Optimal security reductions for unique signatures (Coron, Eurocrypt 2002) and their generalization, i.e., efficiently re-randomizable signatures (Hofheinz et al. PKC 2012 & Bader et al. Eurocrypt 2016) have been well studied in the literature. Particularly, it has been shown that under a non-interactive hard assumption, any security reduction (with or without random oracles) for a unique signature scheme or an efficiently re-randomizable signature scheme must loose a factor of at least qsqs in the security model of existential unforgeability against chosen-message attacks (EU-CMA), where qsqs denotes the number of signature queries. Note that the number qsqs can be as large …
Bridge Text And Knowledge By Learning Multi-Prototype Entity Mention Embedding, Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li
Bridge Text And Knowledge By Learning Multi-Prototype Entity Mention Embedding, Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li
Research Collection School Of Computing and Information Systems
Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative …
Representativeness-Aware Aspect Analysis For Brand Monitoring In Social Media, Lizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Xu, Ta-Seng Chua
Representativeness-Aware Aspect Analysis For Brand Monitoring In Social Media, Lizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Xu, Ta-Seng Chua
Research Collection School Of Computing and Information Systems
Owing to the fast-responding nature and extreme success of social media, many companies resort to social media sites for monitoring their brands’ reputation and the opinions of general public. To help companies monitor their brands, in this work, we delve into the task of extracting representative aspects and posts from users’ free-text posts in social media. Previous efforts have treated it as a traditional information extraction task, and forgo the specific properties of social media, such as the possible noise in user generated posts and the varying impacts; In contrast, we extract aspects by maximizing their representativeness, which is a …
Well-Tuned Algorithms For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen, Kun Lu
Well-Tuned Algorithms For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen, Kun Lu
Research Collection School Of Computing and Information Systems
The Team Orienteering Problem with Time Windows (TOPTW) is the extension of the Orienteering Problem (OP) where each node is limited by a predefined time window during which the service has to start. The objective of the TOPTW is to maximize the total collected score by visiting a set of nodes with a limited number of paths. We propose two algorithms, Iterated Local Search and a hybridization of Simulated Annealing and Iterated Local Search (SAILS), to solve the TOPTW. As indicated in multiple research works on algorithms for the OP and its variants, determining appropriate parameter values in a statistical …
Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu
Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu
Research Collection School Of Computing and Information Systems
The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.The challenging question: what products should be recommended for a given time period to maximize …
Will This Localization Tool Be Effective For This Bug? Mitigating The Impact Of Unreliability Of Information Retrieval Based Bug Localization Tools, Tien-Duy B. Le, Ferdian Thung, David Lo
Will This Localization Tool Be Effective For This Bug? Mitigating The Impact Of Unreliability Of Information Retrieval Based Bug Localization Tools, Tien-Duy B. Le, Ferdian Thung, David Lo
Research Collection School Of Computing and Information Systems
Information retrieval (IR) based bug localization approaches process a textual bug report and a collection of source code files to find buggy files. They output a ranked list of files sorted by their likelihood to contain the bug. Recently, several IR-based bug localization tools have been proposed. However, there are no perfect tools that can successfully localize faults within a few number of most suspicious program elements for every single input bug report. Therefore, it is difficult for developers to decide which tool would be effective for a given bug report. Furthermore, for some bug reports, no bug localization tools …
Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng
Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng
Research Collection School Of Computing and Information Systems
Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set …
On Efficiently Finding Reverse K-Nearest Neighbors Over Uncertain Graphs, Yunjun Gao, Xiaoye Miao, Gang Chen, Baihua Zheng, Deng Cai, Huiyong Cui
On Efficiently Finding Reverse K-Nearest Neighbors Over Uncertain Graphs, Yunjun Gao, Xiaoye Miao, Gang Chen, Baihua Zheng, Deng Cai, Huiyong Cui
Research Collection School Of Computing and Information Systems
Reverse k-nearest neighbor (RkNN) query on graphs returns the data objects that take a specified query object q as one of their k-nearest neighbors. It has significant influence in many real-life applications including resource allocation and profile-based marketing. However, to the best of our knowledge, there is little previous work on RkNN search over uncertain graph data, even though many complex networks such as traffic networks and protein–protein interaction networks are often modeled as uncertain graphs. In this paper, we systematically study the problem of reversek-nearest neighbor search on uncertain graphs (UG-RkNN search for short), where graph edges contain uncertainty. …
Pivot-Based Metric Indexing, Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, Keyu Yang
Pivot-Based Metric Indexing, Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, Keyu Yang
Research Collection School Of Computing and Information Systems
The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivot-based indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies …
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias, …
Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis
Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision-making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries, with strong practical relevance and important applications, that have a computational geometric nature. The tutorial will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.
Basket-Sensitive Personalized Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Basket-Sensitive Personalized Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the …
Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw
Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a …
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Research Collection School Of Computing and Information Systems
Feature selection (FS) is an important technique in machine learning and data mining, especially for large scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, …
Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok
Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok
Research Collection School Of Computing and Information Systems
Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps-Ookla Speedtest and MobiPerf-have their RTT measurements inflated. We build three test apps that cover three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete …
Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng
Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng
Research Collection School Of Computing and Information Systems
Equipped with various sensors and intelligent systems, modern cars turn into entities with connectivity, autonomy, and safety. Car rental/car sharing is an innovative transportation concept and integral in today's urban living. It enables users to access a fleet of vehicles located throughout cities. Complementing public transportation, the car-sharing service helps people to meet their transportation needs economically and in an environmentally responsible manner. When a customer wants to rent a car from a rental company or an owner wants to share a private car with his/her friends or family members, the customer or the user should gain admission to the …
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Research Collection School Of Computing and Information Systems
We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack …
Secure Encrypted Data Deduplication With Ownership Proof And User Revocation, Wenxiu Ding, Zheng Yan, Robert H. Deng
Secure Encrypted Data Deduplication With Ownership Proof And User Revocation, Wenxiu Ding, Zheng Yan, Robert H. Deng
Research Collection School Of Computing and Information Systems
Cloud storage as one of the most important cloud services enables cloud users to save more data without enlarging its own storage. In order to eliminate repeated data and improve the utilization of storage, deduplication is employed to cloud storage. Due to the concern about data security and user privacy, encryption is introduced, but incurs new challenge to cloud data deduplication. Existing work cannot achieve flexible access control and user revocation. Moreover, few of them can support efficient ownership proof, especially public verifiability of ownership. In this paper, we propose a secure encrypted data deduplication scheme with effective ownership proof …
Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu
Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu
Research Collection School Of Computing and Information Systems
Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations …
Integrating Apache Spark And R For Big Data Analytics On Solving Geographic Problems, Mengqi Zhang, Tin Seong Kam
Integrating Apache Spark And R For Big Data Analytics On Solving Geographic Problems, Mengqi Zhang, Tin Seong Kam
Research Collection School Of Computing and Information Systems
With the advent ofdigital technology and smart devices, a flood of digital data is beinggenerated every day. This huge amount of data not only records the historyactivities but also provides future valuable information for organizations andbusinesses. However, the true values of these data will not be fullyappreciated until they have been processed, analyzed and the analysis resultsbeen communicated to decision makers in a business friendly manner.In view of thisneed, big data has been one of the major research focus in the academicresearch community especially in the field of computer science and the softwarevendor as well as the big data service …
Predicting Potential Alzheimer Medical Condition In Elderly Using Iot Sensors - Case Study, Zhi Hao Kevin Chong, Yu Xuan Tee, Ling Jing Toh, Shi Jia Phang, Jie Ying Liew, Bertran Queck, Swapna Gottipati
Predicting Potential Alzheimer Medical Condition In Elderly Using Iot Sensors - Case Study, Zhi Hao Kevin Chong, Yu Xuan Tee, Ling Jing Toh, Shi Jia Phang, Jie Ying Liew, Bertran Queck, Swapna Gottipati
Research Collection School Of Computing and Information Systems
Ageing population would cause profound problems and the impact is already being felt today in many developed countries such as Singapore. The main concern for the Government is to help the citizens with active ageing through home ownership and good healthcare. With Internet of Things (IoT) gaining traction globally, Singapore is set to take advantage of this technology and leverage it to extend its capabilities towards a graceful Ageing-In-Place for the elderly. This ties in nicely with the expertise of SHINE Seniors project by SMU-iCity Lab, which integrates IT with healthcare in ways that creates innovative IT health solutions that …
Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose …
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang
Research Collection School Of Computing and Information Systems
Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neura lNetwork (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to …
Deepfacade: A Deep Learning Approach To Facade Parsing, Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi
Deepfacade: A Deep Learning Approach To Facade Parsing, Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using bounding boxes generated by the Region …