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Sparse Dictionaries for Semantic Segmentation [chapter]

Lingling Tao, Fatih Porikli, René Vidal
2014 Lecture Notes in Computer Science  
Moreover, we propose a new method for jointly learning the CRF parameters, object classifiers and the visual dictionary.  ...  Moreover, we propose a new method for jointly learning the CRF parameters, object classifiers and the visual dictionary.  ...  We thank Florent Couzinié-Devy for interesting discussions about the gradient computation.  ... 
doi:10.1007/978-3-319-10602-1_36 fatcat:ys3g4artavd4jdfck4h2jptsgy

A Mixed Semantic Features Model for Chinese NER with Characters and Words [chapter]

Ning Chang, Jiang Zhong, Qing Li, Jiang Zhu
2020 Lecture Notes in Computer Science  
In this paper, we introduce the self-attention mechanism into the BiLSTM-CRF neural network structure for Chinese named entity recognition with two embedding.  ...  However, using a single granularity representation would suffer from the problems of out-of-vocabulary and word segmentation errors, and the semantic content is relatively simple.  ...  As illustrated in Fig. 2 , the architecture of our model mainly consists of character and word embedding, Bi-LSTM network with self-attention and CRF for tagging.  ... 
doi:10.1007/978-3-030-45439-5_24 fatcat:rrp52ui4u5hvbcpo7hcimtm3gq

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery

Narges Ahmidi, Lingling Tao, Shahin Sefati, Yixin Gao, Colin Lea, Benjamin Bejar Haro, Luca Zappella, Sanjeev Khudanpur, Rene Vidal, Gregory D. Hager
2017 IEEE Transactions on Biomedical Engineering  
These techniques comprise four temporal approaches for joint segmentation and classification: Hidden Markov Model, Sparse HMM, Markov semi-Markov Conditional Random Field, and Skip-Chain CRF; and two feature-based  ...  We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS.  ...  Grace Chen and Carol Reiley for their contribution to data collection and annotation. We appreciate Simon P. DiMaio and Intuitive Surgical Inc. for their help in data collection and Balazs P.  ... 
doi:10.1109/tbme.2016.2647680 pmid:28060703 pmcid:PMC5559351 fatcat:xkhuzvvi7zckrf6at3dint7nsy

End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding [article]

Effrosyni Mavroudi, Divya Bhaskara, Shahin Sefati, Haider Ali, René Vidal
2018 arXiv   pre-print
We introduce an end-to-end algorithm for jointly learning the weights of the CRF model, which include action classification and action transition costs, as well as an overcomplete dictionary of mid-level  ...  This results in a CRF model that is driven by sparse coding features obtained using a discriminative dictionary that is shared among different actions and adapted to the task of structured output learning  ...  We would like to thank Colin Lea and Lingling Tao for their insightful comments and for their help with the JIGSAWS dataset, and Vicente Ordóñez for his useful feedback during this research collaboration  ... 
arXiv:1801.09571v1 fatcat:mqcbwroiu5bqbmmb2hp3vvluim

Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism

Kaihong Zheng, Lingyun Sun, Xin Wang, Shangli Zhou, Hanbin Li, Sheng Li, Lukun Zeng, Qihang Gong
2021 IEEE Access  
A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information.  ...  This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers.  ...  We use the jieba word segmentation tool to divide the corpus text and add the counted entity names to the dictionary. The word segmentation results obtained have an precision of 96.2%.  ... 
doi:10.1109/access.2021.3123154 fatcat:ojgt775qvvhtji2i2pquyjgccy

Top-Down Visual Saliency via Joint CRF and Dictionary Learning

Jimei Yang, Ming-Hsuan Yang
2017 IEEE Transactions on Pattern Analysis and Machine Intelligence  
In this paper, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a discriminative dictionary.  ...  Top-down visual saliency facilities object localization by providing a discriminative representation of target objects and a probability map for reducing the search space.  ...  Acknowledgments This work is supported in part by the NSF CAREER Grant #1149783 and NSF IIS Grant #1152576.  ... 
doi:10.1109/tpami.2016.2547384 pmid:28113265 fatcat:ltzmdfgjrragvi2mvojupwxyqm

Top-down visual saliency via joint CRF and dictionary learning

Jimei Yang, Ming-Hsuan Yang
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
In this paper, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a discriminative dictionary.  ...  Top-down visual saliency facilities object localization by providing a discriminative representation of target objects and a probability map for reducing the search space.  ...  Acknowledgments This work is supported in part by the NSF CAREER Grant #1149783 and NSF IIS Grant #1152576.  ... 
doi:10.1109/cvpr.2012.6247940 dblp:conf/cvpr/YangY12 fatcat:edyis7mjgbgjnkn3j22xtbw4c4

Visual Dictionary Learning for Joint Object Categorization and Segmentation [chapter]

Aastha Jain, Luca Zappella, Patrick McClure, René Vidal
2012 Lecture Notes in Computer Science  
In this paper, we formulate the semantic segmentation problem as a joint categorization, segmentation and dictionary learning problem.  ...  However none of these dictionaries are learnt for joint object categorization and segmentation.  ...  Moreover, this dictionary is kept fixed while learning the categorization and segmentation parameters of the CRF.  ... 
doi:10.1007/978-3-642-33715-4_52 fatcat:b4b7al4p6bcb5f7r5arabcrqje

Table of Contents

2019 IEEE transactions on multimedia  
Image/Video/Graphics Analysis and Synthesis Joint CRF and Locality-Consistent Dictionary Learning for Semantic Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . .  ...  Video Surveillance and Semantic Analysis Learning Attentional Recurrent Neural Network for Visual Tracking . . . . . . . . . Q. Wang, C. Yuan, J. Wang, and W.  ... 
doi:10.1109/tmm.2019.2904597 fatcat:xptgvh44knfizl2tt3wsr4sgxe

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, Xing Xie
2019 The World Wide Web Conference on - WWW '19  
In this paper, we propose a neural approach for CNER. First, we introduce a CNN-LSTM-CRF neural architecture to capture both local and long-distance contexts for CNER.  ...  Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming.  ...  These methods are based on LSTM-CRF framework, where LSTM is used to learn hidden representations of characters and CRF is used for joint labeling decoding.  ... 
doi:10.1145/3308558.3313743 dblp:conf/www/WuLWHX19 fatcat:22gxznga7nfjre3zcisxkfouny

Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

Yue Wu, Junyi Zhang
2018 Future Internet  
on attention and semantic features.  ...  With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance.  ...  Acknowledgments: We would like to thank our colleges for their suggestions and help. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/fi10100095 fatcat:c5bcchfnjnglbapfzhcc5zjqbq

Uyghur morphological analysis using joint conditional random fields: Based on small scaled corpus

Ghalip Abdukerim, Eziz Tursun, Yating Yang, Xiao Li
2019 Discrete and Continuous Dynamical Systems. Series S  
It is necessary to provide various information for other tasks of natural language processing including syntactic analysis, machine translation, automatic summarization, and semantic analysis, etc.  ...  As a fundamental research in the field of natural language processing, the Uyghur morphological analysis is used mainly to determine the part of speech (POS) and segmental morphemes (stem and affix) of  ...  An instance of training corpus for morphological tagging consists of the segmented morpheme sequences of a word and corresponding tag sequences, equivalent to each word and corresponding POS tag in the  ... 
doi:10.3934/dcdss.2019055 fatcat:3p4cc6dukzfhlj4uqupo3q7w5i

Modeling spatial layout for scene image understanding via a novel multiscale sum-product network

Zehuan Yuan, Hao Wang, Limin Wang, Tong Lu, Shivakumara Palaiahnakote, Chew Lim Tan
2016 Expert systems with applications  
Semantic image segmentation is challenging due to the large intra-class variations and the complex spatial layouts inside natural scenes.  ...  We conduct experiments on two challenging benchmarks consisting of the MSRC-21 dataset and the SIFT FLOW dataset.  ...  Acknowledgment The work described in this paper was supported by the Natural Science Foundation of China under Grant No. 61272218 and No. 61321491 , and the Program for New Century Excellent Talents  ... 
doi:10.1016/j.eswa.2016.07.015 fatcat:nk7ibgsvjbcf5kjnxxsizyj6fy

An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records

Luqi Li, Jie Zhao, Li Hou, Yunkai Zhai, Jinming Shi, Fangfang Cui
2019 BMC Medical Informatics and Decision Making  
It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.  ...  Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map.  ...  Availability of data and materials The datasets used in this study are adopted from the Chinese EMR named entity recognition task in China Conference on Knowledge Graph and Semantic Computing in 2018 (  ... 
doi:10.1186/s12911-019-0933-6 pmid:31801540 pmcid:PMC6894110 fatcat:c6wgahdbdzccfpcmon3gus4bxi

End-to-End Chinese Parsing Exploiting Lexicons [article]

Yuan Zhang, Zhiyang Teng, Yue Zhang
2020 arXiv   pre-print
In this paper, we propose an end-to-end Chinese parsing model based on character inputs which jointly learns to output word segmentation, part-of-speech tags and dependency structures.  ...  Chinese parsing has traditionally been solved by three pipeline systems including word-segmentation, part-of-speech tagging and dependency parsing modules.  ...  LSAN-CRF (Gan and Zhang, 2019 ) uses a local self-attention network instead of BiLSTM.  ... 
arXiv:2012.04395v1 fatcat:ps3lyosizve2rjrjhv7vff5k3i
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