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Sparse Dictionaries for Semantic Segmentation [chapter]

Lingling Tao, Fatih Porikli, René Vidal
2014 Lecture Notes in Computer Science  
In this paper, we propose a CRF model that incorporates a DSDL based top-down cost for semantic segmentation.  ...  In this paper, we propose a CRF model that incorporates a DSDL based top-down cost for semantic segmentation.  ...  We thank Florent Couzinié-Devy for interesting discussions about the gradient computation.  ... 
doi:10.1007/978-3-319-10602-1_36 fatcat:ys3g4artavd4jdfck4h2jptsgy

DUTIR at the CCKS-2018 Task1: A Neural Network Ensemble Approach for Chinese Clinical Named Entity Recognition

Ling Luo, Nan Li, Shuaichi Li, Zhihao Yang, Hongfei Lin
2018 China Conference on Knowledge Graph and Semantic Computing  
., CNN-CRF, BiLSTM-CRF, BiLSTM-CNN-CRF, BiLSTM+CNN-CRF and Lattice LSTM). In this approach, the various features (i.e., stroke, word segmentation and dictionary features) are adopted.  ...  The 2018 China conference on knowledge graph and semantic computing (CCKS) challenge sets up a task for clinical named entity recognition (CNER).  ...  The overall architecture of the NN-CRF model CNN-CRF model. In the convolutional neural network (CNN) with a CRF layer model, a convolution operation is applied to produce local features.  ... 
dblp:conf/ccks/LuoLLYL18 fatcat:e6r6g53xajgenjcdohwedebzuu

Learning a Dictionary of Shape Epitomes with Applications to Image Labeling

Liang-Chieh Chen, George Papandreou, Alan L. Yuille
2013 2013 IEEE International Conference on Computer Vision  
The first main contribution of this paper is a novel method for representing images based on a dictionary of shape epitomes.  ...  Our resulting hierarchical CRFs efficiently capture both local and global class co-occurrence properties.  ...  Adapting CRFs for segmentation templates Having learned the dictionary of shape epitomes, we now proceed to show how we can build models for image labeling on top of it.  ... 
doi:10.1109/iccv.2013.49 pmid:26321886 pmcid:PMC4550222 dblp:conf/iccv/ChenPY13 fatcat:5e6utyjenvbe3fie5cskdzciti

Radical-Enhanced Chinese Character Embedding [article]

Yaming Sun, Lei Lin, Duyu Tang, Nan Yang, Zhenzhou Ji, Xiaolong Wang
2014 arXiv   pre-print
We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character.  ...  It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage.  ...  Instead of hand-crafting feature, we leverage the learned character embedding as features for Chinese word segmentation with Neural CRF (Turian et al., 2010; Zheng et al., 2013) .  ... 
arXiv:1404.4714v1 fatcat:kvmujth2kjhuvpkbxjplgyqz3u

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

Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

Zhe Kong, Changxi Yue, Ying Shi, Jicheng Yu, Changjun Xie, Lingyun Xie
2021 IEEE Access  
semantic meaning and essential information.  ...  This algorithm is composed of a dictionary-based method, a language technology platform (LTP) tool, and the bidirectional encoder representations from a transformers-conditional random field (BERT-CRF)  ...  Word vectors represent the semantic meaning contained in the text and can be evaluated with semantic-related tasks.  ... 
doi:10.1109/access.2021.3063354 fatcat:i6pc5ewkhvapvbahtjedunvqgq

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  ...  Conclusion-Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons  ...  Vagvolgyi for his contribution to the data collection software. We also thank Jonathan Jones, Amanda Edwards, and Dr. Austin Reiter for proofreading the manuscript.  ... 
doi:10.1109/tbme.2016.2647680 pmid:28060703 pmcid:PMC5559351 fatcat:xkhuzvvi7zckrf6at3dint7nsy

Word Segmentation for Classical Chinese Buddhist Literature

Yu-Chun Wang
2020 Journal of the Japanese Association for Digital Humanities  
In this paper, we adopt unsupervised and supervised learning techniques to build Classical Chinese word segmentation approaches for processing Buddhist literature.  ...  Conditional random fields (CRF) are used to generate supervised models for Classical Chinese word segmentation. The performance of our word segmentation approach achieves an F-score of up to 0.9396.  ...  It is difficult for a supervised learning model to distinguish these two cases without syntactic and semantic clues.  ... 
doi:10.17928/jjadh.5.2_154 fatcat:4uvmjlipxvaijpncxynzyhuqlq

Class segmentation and object localization with superpixel neighborhoods

Brian Fulkerson, Andrea Vedaldi, Stefano Soatto
2009 2009 IEEE 12th International Conference on Computer Vision  
Instead of operating at the pixel level, we advocate the use of superpixels as the basic unit of a class segmentation or pixel localization scheme.  ...  We propose a method to identify and localize object classes in images.  ...  Top two rows: Without CRF. Bottom two rows: With CRF. Figure 3 . 3 PASCAL VOC 2007 + CRF. Some selected segmentations for PASCAL.  ... 
doi:10.1109/iccv.2009.5459175 dblp:conf/iccv/FulkersonVS09 fatcat:wcgp3w3bvvaofnnj62uq3ytb34

Latent Semantics Local Distribution for CRF-based Image Semantic Segmentation

Giuseppe Passino, Ioannis Patras, Ebroul Izquierdo
2009 Procedings of the British Machine Vision Conference 2009  
Then, in a CRF-based formulation we learn both the appearance for each semantic category and the neighbouring relations between patches.  ...  This paper proposes a method that combines a region-based probabilistic graphical model that builds on the recent success of Conditional Random Fields (CRFs) in the problem of semantic segmentation, with  ...  Paper contributions and overview In the light of what said so far, we propose a method to integrate distributed information to local patch analysis in a CRF-based framework for semantic segmentation.  ... 
doi:10.5244/c.23.26 dblp:conf/bmvc/PassinoPI09 fatcat:ohu57d6knbdztoxyzweacnkulu

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

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

Yue Wu, Junyi Zhang
2018 Future Internet  
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.  ...  on attention and semantic features.  ...  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

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.  ...  Consent for publication Not applicable.  ... 
doi:10.1186/s12911-019-0933-6 pmid:31801540 pmcid:PMC6894110 fatcat:c6wgahdbdzccfpcmon3gus4bxi

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

Aastha Jain, Luca Zappella, Patrick McClure, René Vidal
2012 Lecture Notes in Computer Science  
The CRF energy consists of a bottom-up segmentation cost, a top-down bag of (latent) words categorization cost, and a dictionary learning cost.  ...  In this paper, we formulate the semantic segmentation problem as a joint categorization, segmentation and dictionary learning problem.  ...  For instance, the work of [18] combines CRFs with dictionary learning for object detection purposes, but it does not address segmentation or categorization.  ... 
doi:10.1007/978-3-642-33715-4_52 fatcat:b4b7al4p6bcb5f7r5arabcrqje

Top-Down Visual Saliency via Joint CRF and Dictionary Learning

Jimei Yang, Ming-Hsuan Yang
2017 IEEE Transactions on Pattern Analysis and Machine Intelligence  
The proposed model is formulated based on a CRF with latent variables. By using sparse codes as latent variables, we train the dictionary modulated by CRF, and meanwhile a CRF with sparse coding.  ...  In this paper, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a discriminative dictionary.  ...  local observations with the dictionary.  ... 
doi:10.1109/tpami.2016.2547384 pmid:28113265 fatcat:ltzmdfgjrragvi2mvojupwxyqm
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