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Fine-Grained Generation for Zero-Shot Learning 27th

Fine-Grained Generation for Zero-Shot Learning

引用

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Sun, Weimin Xu, Jieping Yang, Gang Key Lab of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9783030678319

Although zero-shot learning (ZSL) has achieved success in recognizing unseen classes images, most previous studies focus on feature projection from one domain to another, neglecting the domain shift problem caused by differences in classes. In this paper, we propose two novel methods in terms of attributes and features for ZSL problem. The first is Sample-Level Attribute Generation via GAN (SLA-GAN) that generates sample-level attributes. Considering the influence of lighting, environment, weather and other factors, the description based on class-level attributes will be biased. SLA-GAN generates sample-level attributes to describe each sample. Specially, we train a conditional Wasserstein GAN in which the generator synthesizes sample-level attributes from features and the discriminator distinguishes the sample-level from class-level attributes. Apart from that, we propose a novel method Fine-Grained Sample Generation via GAN (FGS-GAN) which can generate the unseen samples from the most similar samples of seen classes. Finally, we replace the class-level attributes with sample-level attributes in FGS-GAN and analyze the effects of sample-level attributes on synthesized features. Experiments on five benchmark data set show that our models outperform state-of-the-art methods in ZSL. © 2021, Springer Nature Switzerland AG.

关键词： Zero-shot learning

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A collaborative join scheme on a MIC-based heterogeneous platform

A collaborative join scheme on a MIC-based heterogeneous pla...

引用

Lecture Notes in Computer Science

作者： Zhou, Kailai Sun, Hui Chen, Hong Wu, Tianzhen Li, Cuiping Key Lab of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China School of Computer and Information Southwest Forestry University Kunming China

ISBN: (纸本)9783319458168

Join is one of the most important operations in data analytics systems. Prior works focus mainly on join optimization using GPUs, but little is known about performance impact on the MICs. In order to investigate potential benefits of the use of MIC accelerators in improving performance of join operation, in this paper we design a join scheme with a CPU and MICs working collaboratively. This scheme includes task partitioning, a data transfer mode, join algorithm design. Experimental results show that our collective join scheme is effective for a heterogeneous platform with two Xeon Phi cards, and can improve performance by up to 30 % over the CPU-only platform. © Springer International Publishing Switzerland 2016.

关键词： data transfer

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Extracting multi-records from web pages

Extracting multi-records from web pages

引用

4th International Conference on Semantics, knowledge, and Grid, SKG 2008

作者： Tian, Xia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Resource Management Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769534015

Extracting multi-records from web pages is useful, it allows us to integrate information from multiple sources to provide value-added services. Existing techniques still have some limitations because of their several restrictions and accuracy. This paper proposes a new method to perform multi-records extraction task automatically. Firstly, the HTML tag tree is build based on an embedded browser interface to solve the AJAX problem. Secondly, data regions are found out by data chunk comparison, and simple tree matching method is proposed to compute the chunk similarity. Finally, the main data region is determined and the multi-records are extracted out. Experimental results show that our method dramatically outperforms other existing methods, and it can extract multi-records from pages very accurately. © 2008 IEEE.

关键词： Websites

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Implementation and performance of VoIP interception based on SIP session border controller

Implementation and performance of VoIP interception based on...

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作者： Yang, Menghui Liu, Hua Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Resources Management Renmin University of China Beijing 100872 China

In an effort to provide lawful interception for session initiation protocol (SIP) voice over Internet protocol (VoIP), an interception architecture using session border controller (SBC) is proposed. Moreover, a prototype based on the proposed architecture is implemented. A testbed is set up and tests are carried out in order to analyze the performance and the capability of function entities and interfaces in the proposed architecture. Test results show that SBC interception capability in SIP signaling is superior to that in real-time transport protocol (RTP) media stream. In order to eliminate the possible bottleneck of RTP packets interception in SBC, an analytic model is proposed to investigate the mechanism in which RTP packet's traffics are shared among different SBC media functions. Analysis results show that multiple SBC media functions can share the RTP packets arrival and can significantly decrease RTP packets service time in SBC. Test results also show that delivery function, collect function and their interfaces in the proposed interception architecture have corresponding interception performance and capability with SBC. © 2013 Springer Science+Business Media New York.

关键词： Controllers

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renmin university of china at ImageCLEF 2013 scalable concept image annotation

Renmin University of China at ImageCLEF 2013 scalable concep...

引用

2013 Cross Language Evaluation Forum Conference, CLEF 2013

作者： Li, Xirong Liao, Shuai Liu, Binbin Yang, Gang Jin, Qin Xu, Jieping Du, Xiaoyong Key Lab of Data Engineering and Knowledge Engineering MOE Japan Multimedia Computing Lab School of Information Renmin University of China Zhongguancun Street 59 Beijing100872 China

In this paper we describe our image annotation system par ticipated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including single feature and multi-feature kNN classifiers and histogram intersection ker nel SVMs, all of which are learned from the provided 250K web images and provided features with no extra manual verification. These base clas sifiers are combined into a stacked model, with the combination weights optimized to maximize the geometric mean of F-samples, F-concepts, and AP-samples metrics on the provided development set. By varying the configuration of the system, we submitted five runs. Evaluation re sults show that for all of our runs, model stacking with optimized weights performs best. Our system can annotate diverse Internet images purely based on the visual content, at the following accuracy level: F-samples of 0.290, F-concepts of 0.304, and AP-samples of 0.380. What is more, a system-to-system comparison reveals that our system and the best sub mission this year are complementary with respect to the best annotated concepts, suggesting the potential for future improvement.

关键词： Image annotation

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Cross-codebook image classification

Cross-codebook image classification

引用

14th Pacific-Rim Conference on Multimedia, PCM 2013

作者： Liao, Shuai Li, Xirong Du, Xiaoyong Key Lab of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China Beijing 100872 China State Key Lab of Software Development Environment Beijing 100191 China

ISBN: (纸本)9783319037301

Representing images by bag of visual codes (BoVC) features has been the cornerstone of state-of-the-art image classification system. Since the BoVC features depend on a precomputed codebook in use, when the codebook applied to test images differs from the codebook of an existing image classification system, the system becomes inapplicable. To resolve the codebook incompatibility problem, we propose in this paper cross-codebook image classification. This is achieved by transforming BoVC features derived from one codebook to make them compatible with another codebook. Two BoVC transform methods, i.e., code-reassignment and least squares, are studied. Experiments on a popular image classification benchmark set show that both methods are better than random guess when crossing the codebooks. In particular, when the BoVC features are transformed from a higher dimension to a relatively small dimension, cross-codebook image classification has a similar performance compared to within-codebook image classification, with a relative performance loss of 1.3% only. The results justify the feasibility of the proposed cross-codebook image classification. © Springer International Publishing Switzerland 2013.

关键词： Image classification

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Towards structured semantic embedding of multimedia 15

Towards structured semantic embedding of multimedia

引用

7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Li, Xirong Huo, Yujia Lan, Weiyu He, Xixi Liao, Shuai Key Lab of Data Engineering and Knowledge Engineering Renmin University of China 100872 China Multimedia Computing Lab School of Information Renmin University of China 100872 China

ISBN: (纸本)9781450335287

This abstract paper sketches our research towards Struc-tured Semantic Embedding of multimedia data. Though a tag may have multiple senses with completely different visual imagery, current semantic embedding methods represent the tag by a single vector regardless of its senses. We challenge this convention, arguing the importance of adding seman-tic structures into semantic embedding. In particular, we develop Hierarchical Semantic Embedding, a simple model that exploits the WordNet hierarchy to make the semantic embedding structured to some extent. We demonstrate the viability of structured semantic embedding for tag disam-biguation and zero-shot image tagging. © 2015 ACM.

关键词： Zero-shot learning

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Parallel hybrid join algorithm on GPU 21

Parallel hybrid join algorithm on GPU

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guo, Chengxin Chen, Hong Zhang, Feng Li, Cuiping Key Lab of Data Engineering and Knowledge Engineering Ministry of Education School of Information Renmin University of China Beijing China

ISBN: (纸本)9781728120584

In data analytics applications, join is a general and time consuming operation. Optimizing join algorithms can benefit the query processing significantly. The emerging of GPUs provides a massive parallelism solution for improving the performance of the join operation. The hash join (HJ) and sort merge join (SMJ), which are two widely used join algorithms, have been proved effective for efficient join processing on the GPUs. Both algorithms have their own advantages and drawbacks, offering the chance of combining the advantages of HJ and SMJ on GPUs. In processing join operation on GPUs, data need to be transmitted between the CPU and the GPU due to the discrete GPU memory design, which causes performance degradation because of the high PCIe data transfer overhead. As GPUs are becoming more powerful than before, the performance gap between data transmission and GPU execution increases, which makes it even harder to implement an efficient join on GPUs. In this paper, we focus on the optimization of join algorithms on GPUs. We propose the Parallel Hybrid Join algorithm on GPUs(PHYJ) to combine the advantages of HJ and SMJ, and overlap the data communication and GPU execution with a pipeline mechanism. In our evaluation, the PHYJ shows up to 1.72X and 1.55X speedup over the up-to-date HJ and SMJ algorithms respectively on a NVIDIA GTX 1080ti-Pascal GPU. On the TitanV-Volta GPU, up to 1.54X and 1.42X improvements can be achieved over the baseline HJ and SMJ algorithms respectively. © 2019 IEEE.

关键词： Graphics processing unit

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In-memory join algorithms on GPUs for large-data 21

In-memory join algorithms on GPUs for large-data

引用

作者： Guo, Chengxin Chen, Hong Key Lab of Data Engineering and Knowledge Engineering of Ministry of Education School of Information Renmin University of China Beijing China

ISBN: (纸本)9781728120584

In traditional databases, join is one of the most computationally expensive operations in query processing. During the past years, GPU has been adopted to improve the performance of join processing because of the features of massive parallelism and high memory bandwidth. Limited by the capacity of GPU memory and the absence of virtual memory management, however, handling the relations that exceed the capacity of the GPU memory is a challenge for GPU-based join algorithms. Because of the high computing throughput provided by GPUs and the low bandwidth of data communication between the CPUs and the GPUs, data have to be partitioned to fit the features of GPUs and to reduce the cost of data transmission. Furthermore, a series of novel techniques have been developed on the GPUs, which can benefit the join algorithms. In this work, we focus on the optimizing of processing join operator on large relations and propose the designs of in-memory hash join and sort-merge join on GPUs. We present the data partition method on the GPUs implemented with a pipeline mechanism. Furthermore, the shuffle instructions and the CUDA streams are applied in our algorithms to best utilize the GPUs. Experimental results indicate that our hash join algorithm delivers up to 1.51X and 1.24X speedup over the state-of-the-art hash join algorithm on CPUs on NVIDIA GTX1080ti-Pascal GPU and TitanV-Volta GPU respectively. For sort-merge join, our algorithm achieves up to 3.52X and 2.21X improvements on the same GPUs respectively compared to the baselines on CPUs. © 2019 IEEE.

关键词： Graphics processing unit

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Classifying tag relevance with relevant positive and negative examples 13

Classifying tag relevance with relevant positive and negativ...

引用

21st ACM International Conference on Multimedia, MM 2013

作者： Li, Xirong Snoek, Cees G. M. Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China Intelligent Systems Lab Amsterdam University of Amsterdam Netherlands

ISBN: (纸本)9781450324045

Image tag relevance estimation aims to automatically deter- mine what people label about images is factually present in the pictorial content. Different from previous works, which either use only positive examples of a given tag or use pos- itive and random negative examples, we argue the impor- Tance of relevant positive and relevant negative examples for tag relevance estimation. We propose a system that se- lects positive and negative examples, deemed most relevant with respect to the given tag from crowd-annotated images. While applying models for many tags could be cumbersome, our system trains efficient ensembles of Support Vector Ma- chines per tag, enabling fast classification. Experiments on two benchmark sets show that the proposed system com- pares favorably against five present day methods. Given extracted visual features, for each image our system can pro- cess up to 3,787 tags per second. The new system is both effective and efficient for tag relevance estimation. Copyright © 2013 ACM.

关键词： Image classification

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