检索结果-内蒙古大学图书馆

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art approaches typically tackle this problem by integrating an elaborate attention mechanism or (part-) localization method into a standard convolutional neural network (CNN). Also in this work the aim is to enhance the performance of a backbone CNN such as ResNet by including three efficient and lightweight components specifically designed for FGVC. This is achieved by using global k-max pooling, a discriminative embedding layer trained by optimizing class means and an efficient localization module that estimates bounding boxes using only class labels for training. The resulting model achieves state-of-the-art recognition accuracies on multiple FGVC benchmark datasets.

关键词： Training Task analysis Automobiles Standards Visualization Birds Testing

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Visual Categorization with 2D-Warping

Fine-Grained Visual Categorization with 2D-Warping

引用

International Conference on pattern recognition

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

The task of fine-grained visual categorization is related to both general object recognition and specialized tasks such as face recognition. Hence, we propose to combine two methods popular for general object recognition and face recognition to build a new model-free system for fine-grained visual categorization. Specifically, we use Local Naive-Bayes Nearest Neighbor as a pre-selection method and 2D-Warping as a refinement step. For the latter, we explore different ways to use the alignments computed by a 2D-Warping algorithm for classification. We demonstrate the performance of our approach on the CUB200-2011 database and show that our approach outperforms the recognition accuracy of current state-of-the-art methods.

关键词： Training Accuracy Visualization Face recognition Databases Birds Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Phrase Model Training for Statistical Machine Translation with Word Lattices of Preprocessing Alternatives 12

Phrase Model Training for Statistical Machine Translation wi...

引用

Workshop on Statistical Machine Translation

作者： Joern Wuebker Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781622765928

In statistical machine translation, word lattices are used to represent the ambiguities in the preprocessing of the source sentence, such as word segmentation for Chinese or morphological analysis for German. Several approaches have been proposed to define the probability of different paths through the lattice with external tools like word segmenters, or by applying indicator features. We introduce a novel lattice design, which explicitly distinguishes between different preprocessing alternatives for the source sentence. It allows us to make use of specific features for each preprocessing type and to lexicalize the choice of lattice path directly in the phrase translation model. We argue that forced alignment training can be used to learn lattice path and phrase translation model simultaneously. On the news-commentary portion of the German→English WMT 2011 task we can show moderate improvements of up to 0.6% Bleu over a state-of-the-art baseline system.

关键词： machine translation crystal lattices Word Pretreatment

来源：评论

学校读者我要写书评

暂无评论

Exploring A Zero-Order Direct Hmm Based on Latent Attention for Automatic Speech recognition

Exploring A Zero-Order Direct Hmm Based on Latent Attention ...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Parnia Bahar Nikita Makarov Albert Zeyer Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

In this paper, we study a simple yet elegant latent variable attention model for automatic speech recognition (ASR) which enables an integration of attention sequence modeling into the direct hidden Markov model (HMM) concept. We use a sequence of hidden variables that establishes a mapping from output labels to input frames. Inspired by the direct HMM model, we assume a decomposition of the label sequence posterior into emission and transition probabilities using zero-order assumption and incorporate both Transformer and LSTM attention models into it. The method keeps the explicit alignment as part of the stochastic model and combines the ease of the end-to-end training of the attention model as well as an efficient and simple beam search. To study the effect of the latent model, we qualitatively analyze the alignment behavior of the different approaches. Our experiments on three ASR tasks show promising results in WER with more focused alignments in comparison to the attention models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding

arXiv

引用

arXiv 2019年

作者： Hanselmann, Harald Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art approaches typically tackle this problem by integrating an elaborate attention mechanism or (part-) localization method into a standard convolutional neural network (CNN). Also in this work the aim is to enhance the performance of a backbone CNN such as ResNet by including three efficient and lightweight components specifically designed for FGVC. This is achieved by using global k-max pooling, a discriminative embedding layer trained by optimizing class means and an efficient bounding box estimator that only needs class labels for training. The resulting model achieves new best state-of-the-art recognition accuracies on the Stanford cars and FGVC-Aircraft datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Multilingual Off-Line Handwriting recognition in Real-World Images

Multilingual Off-Line Handwriting Recognition in Real-World ...

引用

IAPR International Workshop on Document Analysis Systems, DAS

作者： Michal Kozielski Patrick Doetsch Mahdi Hamdani Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We propose a state-of-the-art system for recognizing real-world handwritten images exposing a huge degree of noise and a high out-of-vocabulary rate. We describe methods for successful image demising, line removal, deskewing, deslanting, and text line segmentation. We demonstrate how to use a HMM-based recognition system to obtain competitive results, and how to further improve it using LSTM neural networks in the tandem approach. The final system outperforms other approaches on a new dataset for English and French handwriting. The presented framework scales well across other standard datasets.

关键词： Hidden Markov models Handwriting recognition Standards Training Image segmentation Vocabulary Algorithm design and analysis

来源：评论

学校读者我要写书评

暂无评论

Analysis of Preprocessing Techniques for Latin Handwriting recognition

Analysis of Preprocessing Techniques for Latin Handwriting R...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Hendrik Pesch Mahdi Hamdani Jens Forster Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. This pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. The Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. The results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.

关键词： Hidden Markov models Databases Handwriting recognition Pipelines Text recognition Noise

来源：评论

学校读者我要写书评

暂无评论

Chunk-level reordering of source language sentences with automatically learned rules for statistical machine translation

Chunk-level reordering of source language sentences with aut...

引用

2007 AMTA Workshop on Syntax and Structure in Statistical Translation, SSST 2007 at the 2007 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2007

作者： Zhang, Yuqi Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

In this paper, we describe a source-side reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0.5%-1.8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level. © 2007 Association for Computational Linguistics

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Impact of automatic segmentation on the quality, productivity and self-reported post-editing effort of intralingual subtitles 10

Impact of automatic segmentation on the quality, productivit...

引用

10th International Conference on language Resources and Evaluation, LREC 2016

作者： Álvarez, Aitor Balenciaga, Marina Del Pozo, Arantza Arzelus, Haritz Matamala, Anna Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech-IK4 San-Sebastian Spain Department of Translation Interpreting and East Asian Studies UAB Barcelona Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9782951740891

This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.

关键词： Productivity

来源：评论

学校读者我要写书评

暂无评论

Bi-modal handwritten text recognition (BiHTR) ICPR 2010 contest report

Bi-modal handwritten text recognition (BiHTR) ICPR 2010 cont...

引用

20th International Conference on pattern recognition, ICPR 2010

作者： Pastor, Moisés Paredes, Roberto Pattern Recognition and Human Language Technologies group Department of Information Systems and Computation Technical University of Valencia Cami de Vera s/n. Valencia46022 Spain

ISBN: (纸本)9783642177101

Handwritten text is generally captured through two main modalities: off-line and on-line. Each modality has advantages and disadvantages, but it seems clear that smart approaches to handwritten text recognition (HTR) should make use of both modalities in order to take advantage of the positive aspects of each one. A particularly interesting case where the need of this bi-modal processing arises is when an off-line text, written by some writer, is considered along with the on-line modality of the same text written by another writer. This happens, for example, in computer-assisted transcription of old documents, where on-line text can be used to interactively correct errors made by a main off-line HTR system. In order to develop adequate techniques to deal with this challenging bi-modal HTR recognition task, a suitable corpus is needed. We have collected such a corpus using data (word segments) from the publicly available off-line and on-line IAM data sets. In order to provide the Community with an useful corpus to make easy tests, and to establish baseline performance figures, we have proposed this handwritten bi-modal contest. Here is reported the results of the contest with two participants, one of them achieved a 0% classification error rate, whilst the other participant achieved an interesting 1.5%. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：