In order to improve the limitation of traditional 3D Reconstruction , a kind of image based modeling method is presented . A special object which is visual in both images is used to calibrate camera . An image modeling system is critical tool for CDVS ( Compact Descriptor for Visual Search ) matching test in MPEG-7 . There are uncountable environmental variables in real world and most application developers want to define as them . In recent years a critical need has surfaced for the development of methodologies on the evaluation and understanding of mature image processing algorithms . Effective and efficient algorithm evaluation can be accomplished through algorithm and image modeling . Algorithm performance models can be analytical , empirical or hybrid ( a combination of the two ) . This paper presents CBAM Assistant , a tool that semi-automates the Cost Benefit Analysis Method ( CBAM ) developed by the Software Engineering Institute ( SEI ) at Carnegie Mellon University . CBAM is a process used to estimate the Return on Investment ( ROI ) of various software architectural design strategies . CBAM generally follows the Architectural Trade-off Analysis Method ( ATAM ) also developed by SEI . ATAM aids in defining scenarios and architectural strategies . CBAM aids in quantitative analysis for cost , utility and importance ratings to determine the ROI of each architectural strategy . CBAM Assistant is a web-based system that walks a user through the CBAM process which can be started by using scenarios and architectural strategies created from ATAM . The tool is intended to be used by a facilitator who will provide input from stakeholders . We then benchmark the dataset by designing a novel neural network which can generate density map of an input image . The proposed network consists of three parts namely convolution block attention module ( CBAM ) , scale pyramid module ( SPM ) and deformable convolution module ( DCM ) . Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset , and superiority and effectiveness of our method . We propose Convolutional Block Attention Module ( CBAM ) , a simple yet effective attention module for feed-forward convolutional neural networks . Given an intermediate feature map , our module sequentially infers attention maps along two separate dimensions , channel and spatial , then the attention maps are multiplied to the input feature map for adaptive feature refinement . In this paper , a new method has been used to analyze the Enterprise Architecture scenarios in terms of cost and benefit . The suggested method is presented by a step by step process in which CBAM method has been utilized to measure the cost . This approach ranks enterprise scenarios , using knowledge and experiences of the enterprise experts . Results show that only a limited number of teachers did reach the advanced stages of distance collaboration . They tend to confirm the characteristics of the first stages of the CBAM model , and show that the main factors to consider are technological factors , factors relating to teaching administration , as well as pedagogical , individual , socio-affective and group factors . This paper outlines experiences with using economic criteria to make architecture design decisions . It briefly describes the CBAM ( Cost Benefit Analysis Method ) framework applied to estimate the value of architectural strategies in a NASA project , the ECS . This paper describes the practical difficulties and experiences in applying the method to a large realworld system . Based on Hall 's theory of CBAM , the study on the stages of concerns of the backbone English teachers in the implementation of New Curriculum is carried out . Factors that influence the stages are explored and some suggestions about perfection of courses are put forward . Explicit requirements elicitation and negotiation is needed to be able to appropriately consider and evaluate architecture alternatives and the architecture alternatives need be understood during requirements negotiation . This paper propose the WinCBAM framework , extending an architecture design method , called cost benefit analysis method ( CBAM ) framework to include an explicit requirements negotiation component based on the WinWin methodology . We then provide a retrospective case study that demonstrates the use of the WinCBAM . This paper presents our experience of applying ATAM to a small-scale studio project and analyzes how properly it was adjusted to the project situation , what was done well and what could have done better . This paper also presents a CBAM evaluation for the same studio project that was conducted on the output of the ATAM process . The objective of this evaluation is to justify our decision for .Net over J2EE for the architectural framework for the target software system . One is adding squeeze-and-excitation ( SE ) blocks to the CNN structure that selectively emphasize the most informative channels and suppress the relatively less informative channels by taking advantage of channel dependence . Another method is adding convolutional block attention module ( CBAM ) to implement both channel-wise and spatial attention mechanisms to select important pixels of the feature maps while emphasizing informative channels . In this paper , we propose an encoder-decoder architecture based on the idea of letting the channel-wise and spatial attention blocks share the same latent space representation . Our methods can provide reliable initial segmentation masks for training subsequent segmentation network to generate final segmentation results . In addition , we refined convolutional block attention module ( CBAM ) [ 30 ] to hierarchically extract more category-aware features by capturing global context related information and further promote the propagation process . Experiments on benchmark demonstrate that our proposed method obtains superior performance over the state-of-the-arts . PAL can incorporate the prior knowledge of standard templates to guide the attention-based model for discovering semantic-invariant features when the model pays attention to regions of interest . Furthermore , we also extend the convolutional attention mechanism to speed up the image-to-markup parsing process while achieving competitive performance compared with recurrent attention models . We evaluate the proposed method in the scenario of handwritten-image-to-LaTeX generation , i.e. , converting handwritten mathematical expressions to LaTeX. Experimental results show that our method can significantly improve the generalization performance over standard attention-based encoder-decoder models . In this paper , we present a novel Densely Convolutional Attention Network ( DCAN ) for SISR to improve the quality of reconstructed images . Specifically , we propose a convolutional attention mechanism which adds an weighted skip-connection joint the original convolution . Our network can adaptively recalibrate convolutional-wise features by considering interdependencies among convolutional layers . However , models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary ( OOV ) words . In this paper , we investigate a Convolutional Attention Network called CAN for Chinese NER , which consists of a character-based convolutional neural network ( CNN ) with local-attention layer and a gated recurrent unit ( GRU ) with global self-attention layer to capture the information from adjacent characters and sentence contexts . Also , compared to other models , not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical . Most of the articles in this special issue address the problem of recognition and representation learning . The article titled “ Convolutional Attention Networks for Scene Text Recognition , ” by Shancheng Fang , Hongtao Xie , Yating Yang , Yan Li , and Yongdong Zhang , presents a convolutional attention network ( CAN ) for unconstrained scene text recognition based on a fully Convolutional Neural Network ( CNN ) architecture and the attention mechanism . The article “ Structure-Aware Deep Learning for Product Image Classification , ” by Zhineng Chen , Shanshan Ai , and Caiyan Jia , classifies product images by leveraging the hierarchical structure and attributes existing in online web stores . Considering this critical gap , the focus in this letter is developing an effective EEG signal analysis adaptively applied to subject-independent settings . We present a convolutional recurrent attention model ( CRAM ) that utilizes a convolutional neural network to encode the high-level representation of EEG signals and a recurrent attention mechanism to explore the temporal dynamics of the EEG signals as well as to focus on the most discriminative temporal periods . Extensive experiments on a benchmark multiclass EEG dataset containing four movement intentions indicate that the proposed model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches by at least eight percentage points . A graph structure is first developed to represent the positioning information of EEG nodes . Then a convolutional recurrent attention model learns EEG features from both spatial and temporal dimensions and emphasizes on the most distinguishable temporal periods . We evaluate the proposed approach on two benchmark EEG datasets of motor imagery classification on the subject-independent testing . Motivated by the finding in neuroscience that human possesses the template-searching attention mechanism , we propose to use convolution operation to simulate attentions and give a mathematical explanation of our neural attention model . We then introduce a new network architecture , which combines a recurrent neural network with our convolution-based attention model and further stacks an attention-based neural model to build a hierarchical sentiment classification model . The experimental results show that our proposed models can capture salient parts of the text to improve the performance of sentiment classification at both the sentence level and the document level . In this paper , we design an approach for recognizing styles in photographs by introducing adapted deep convolutional neural networks that are attentive towards strong neural activations . The proposed convolutional attentional units act as a filtering mechanism that conserves activations in convolutional blocks in order to contribute more meaningfully towards the visual style classes . State-of-the-art results were achieved on two large image style datasets , demonstrating the effectiveness of our method . For spatial attention , we learn a saliency mask to allow the model to focus on the most salient parts of the feature maps . For temporal attention , we employ a convolutional LSTM based attention mechanism to identify the most relevant frames from an input video . Further , we propose a set of regularizers to ensure that our attention mechanism attends to coherent regions in space and time . The first part of this thesis discusses the application of artificial intelligence to stock price prediction and the building of another artificial intelligence model for stock price prediction during mergers and acquisitions . It is demonstrated that a combination of Long Short-Term Memory/Recurrent Neural Network + Convolutional Neural Network + Attention ( LSTM/RNN+CNN+Attention ) trained on dailyand minute-stock price and volume data are able to predict future stock prices and stock price changes three times better than traditional non-deep learning methods . Further research has been done on stock price prediction during Mergers and Acquisitions for technology unicorns , finding that when more information on M&A has been added into the input dataset , the accuracy can be improved by about 2 % . further tuned the ABS model with additional features to balance the abstractive and extractive tendency . As the extension of the ABS model , it used a convolutional attention - based encoder and an RNN decoder . used a full s2s RNN model and added the hand - crafted features such as POS tag and NER , to enhance the encoder representation . - lvt5k - 1sent outperforms the state - of - the - art model of with statistically significant improvement on Rouge - 1 . We believe the bidirectional RNN we used to model the source captures richer contextual information of every word than the bag - of - embeddings representation used by namas and chopra in their convolutional attentional encoders , which might explain our superior performance . Further , explicit modeling of important information such as multiple source sentences , word - level linguistic features , using the switch mechanism to point to source words when needed , and hierarchical attention , solve specific problems in summarization , each boosting performance incrementally . To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns , we design the localization branch , which aims to coarsely localize the target to help the regression branch to generate accurate results . Meanwhile , we introduce the global context module into the localization branch to capture long-range dependency for more robustness in large displacement of target . In addition , we design a multi-scale learnable attention module to guide these three branches to exploit discriminative features for better performance . Its performance is on par with the most accurate , time-consuming online-learning model , while its speed is similar to the fastest template-matching method which has sub-optimal accuracy . The core in achieving this is a novel global context module that reliably summarizes and propagates information through the entire video . Compared to previous approaches that only use the first , the last , or a select few frames to guide the segmentation of the current frame , the global context module allows us to use all past frames to guide the processing . For region proposal we propose a novel lightweight cascade structure which can effectively improve RPN proposal quality . For object recognition we re-implement global context modeling with a few modications and obtain a performance boost ( 4.2 % mAP gain on the ILSVRC 2016 validation set ) . Besides , we apply the idea of pre-training extensively and show its importance in both steps . In this paper , a novel Mask R-CNN approach is proposed . Firstly , we combine global context modeling with Mask R-CNN by capturing long-range dependencies . Secondly , we extend the Mask R-CNN approach by fusing a new simplified non-local neural network and Squeeze-and-Excitation block , which helps to achieve long-range dependencies and adaptively recalibrate the channel feature response by modeling the interdependence between channels . As the names are often misspelled or abbreviated , the conventional techniques have failed to detect the noisy names properly . In this paper we propose a global context based framework for handling the noisy names . The framework is tested on a named entity recognition system designed to identify the names from the discussion texts in a homeopathy diagnosis discussion forum . The algorithm for extracting straight lines should have characteristics of high computing speed and high accuracy . The results sho w that the str aight lines extraction algorithm with global context analysis and parametric con trol is very effective and can be used in many applications . The object graph comprises entity , property and relation nodes . We have developed a global context aware algorithm to determine the ontological types of these nodes . Type of one node can help determine the types of other related nodes . We propose a context-aware neural network model for temporal information extraction , with a uniform architecture for event-event , event-timex and timex-timex pairs . A Global Context Layer ( GCL ) , inspired by the Neural Turing Machine ( NTM ) , stores processed temporal relations in the narrative order , and retrieves them for use when the relevant entities are encountered . Relations are then classified in this larger context . The purpose of this work is to devise a neural network with global context feature information for accomplishing medical image segmentation of different tasks . METHODS The proposed global context network ( GC-Net ) consists of two components ; feature encoding and decoding modules . We use multiple convolutions and batch normalization layers in the encoding module . DeepLab is an algorithm based on the fully convolutional networks . However , DeepLab algorithm still has room for improvement , and we design three improved methods : ( 1 ) the global context structure module , ( 2 ) highly efficient decoder module , and ( 3 ) multi-scale feature fusion module . The experimental results show that the three improved methods that we proposed in this paper can make the model obtain more expressive features and improve the accuracy of the algorithm . Meanwhile , we introduce a competition mechanism to handle distortions , in which we build a specialized graph to formulate and handle tracking under distortion as a maximum multi clique problem . Furthermore , a global-local context model is exploited to alleviate underlying distortions during the tracking process . Extensive experiments on the Online Tracking Benchmark show that our tracker can find the optimal target trajectory during the distortion period and retrieve the possibly missing target , consequently outperforms the state-of-the-art methods and improves the performance of CF-based trackers favorably . The example underlying the proof gives a clear illustration of the label bias problem . paragraph : Global Models can be Strictly More Expressive than Local Models Consider a tagging problem where the task is to map an input sequence to a decision sequence . dense - no - i m is the same as dense but takes only the point cloud as input . global is a baseline model that directly regresses the 8 corner locations of a 3D box , as shown in Fig . [ reference ] This baseline result is close to the 2nd - place winner ’s ensemble result ( 25.1% / 45.8 % by FAIRCNN ) . On our baseline result , we further adopt global context modeling and multi - scale testing as in , and ensembling . Our final result on the test - challenge set is 28.2% / 51.5 % , which won the 1st place in the COCO segmentation track of ILSVRC & COCO 2015 competitions . It is followed by a convolution layer to generate the final prediction map in ( d ) . To explain our structure , PSPNet provides an effective global contextual prior for pixel - level scene parsing . The pyramid pooling module can collect levels of information , more representative than global pooling . The application of scSE can improve the adaptability of the feature channel and aggregate relevant spatial information , which greatly reduce false detection and missed detection . In addition , during the final segmentation phase , a spatial Squeeze-and-Excitation module ( sSE ) is used to refine the segmentation . The experimental result reveals that , both detection and instance segmentation have achieved significant improvements in the MS COCO dataset .
In order to improve the limitation of traditional 3D Reconstruction , a kind of image based modeling method is presented . A special object which is visual in both images is used to calibrate camera .
An image modeling system is critical tool for CDVS ( Compact Descriptor for Visual Search ) matching test in MPEG-7 . There are uncountable environmental variables in real world and most application developers want to define as them .
In recent years a critical need has surfaced for the development of methodologies on the evaluation and understanding of mature image processing algorithms . Effective and efficient algorithm evaluation can be accomplished through algorithm and image modeling . Algorithm performance models can be analytical , empirical or hybrid ( a combination of the two ) .
This paper presents CBAM Assistant , a tool that semi-automates the Cost Benefit Analysis Method ( CBAM ) developed by the Software Engineering Institute ( SEI ) at Carnegie Mellon University . CBAM is a process used to estimate the Return on Investment ( ROI ) of various software architectural design strategies . CBAM generally follows the Architectural Trade-off Analysis Method ( ATAM ) also developed by SEI . ATAM aids in defining scenarios and architectural strategies .
CBAM aids in quantitative analysis for cost , utility and importance ratings to determine the ROI of each architectural strategy . CBAM Assistant is a web-based system that walks a user through the CBAM process which can be started by using scenarios and architectural strategies created from ATAM . The tool is intended to be used by a facilitator who will provide input from stakeholders .
We then benchmark the dataset by designing a novel neural network which can generate density map of an input image . The proposed network consists of three parts namely convolution block attention module ( CBAM ) , scale pyramid module ( SPM ) and deformable convolution module ( DCM ) . Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset , and superiority and effectiveness of our method .
We propose Convolutional Block Attention Module ( CBAM ) , a simple yet effective attention module for feed-forward convolutional neural networks . Given an intermediate feature map , our module sequentially infers attention maps along two separate dimensions , channel and spatial , then the attention maps are multiplied to the input feature map for adaptive feature refinement .
In this paper , a new method has been used to analyze the Enterprise Architecture scenarios in terms of cost and benefit . The suggested method is presented by a step by step process in which CBAM method has been utilized to measure the cost . This approach ranks enterprise scenarios , using knowledge and experiences of the enterprise experts .
Results show that only a limited number of teachers did reach the advanced stages of distance collaboration . They tend to confirm the characteristics of the first stages of the CBAM model , and show that the main factors to consider are technological factors , factors relating to teaching administration , as well as pedagogical , individual , socio-affective and group factors .
This paper outlines experiences with using economic criteria to make architecture design decisions . It briefly describes the CBAM ( Cost Benefit Analysis Method ) framework applied to estimate the value of architectural strategies in a NASA project , the ECS . This paper describes the practical difficulties and experiences in applying the method to a large realworld system .
Based on Hall 's theory of CBAM , the study on the stages of concerns of the backbone English teachers in the implementation of New Curriculum is carried out . Factors that influence the stages are explored and some suggestions about perfection of courses are put forward .
Explicit requirements elicitation and negotiation is needed to be able to appropriately consider and evaluate architecture alternatives and the architecture alternatives need be understood during requirements negotiation . This paper propose the WinCBAM framework , extending an architecture design method , called cost benefit analysis method ( CBAM ) framework to include an explicit requirements negotiation component based on the WinWin methodology . We then provide a retrospective case study that demonstrates the use of the WinCBAM .
This paper presents our experience of applying ATAM to a small-scale studio project and analyzes how properly it was adjusted to the project situation , what was done well and what could have done better . This paper also presents a CBAM evaluation for the same studio project that was conducted on the output of the ATAM process . The objective of this evaluation is to justify our decision for .Net over J2EE for the architectural framework for the target software system .
One is adding squeeze-and-excitation ( SE ) blocks to the CNN structure that selectively emphasize the most informative channels and suppress the relatively less informative channels by taking advantage of channel dependence . Another method is adding convolutional block attention module ( CBAM ) to implement both channel-wise and spatial attention mechanisms to select important pixels of the feature maps while emphasizing informative channels . In this paper , we propose an encoder-decoder architecture based on the idea of letting the channel-wise and spatial attention blocks share the same latent space representation .
Our methods can provide reliable initial segmentation masks for training subsequent segmentation network to generate final segmentation results . In addition , we refined convolutional block attention module ( CBAM ) [ 30 ] to hierarchically extract more category-aware features by capturing global context related information and further promote the propagation process . Experiments on benchmark demonstrate that our proposed method obtains superior performance over the state-of-the-arts .
PAL can incorporate the prior knowledge of standard templates to guide the attention-based model for discovering semantic-invariant features when the model pays attention to regions of interest . Furthermore , we also extend the convolutional attention mechanism to speed up the image-to-markup parsing process while achieving competitive performance compared with recurrent attention models . We evaluate the proposed method in the scenario of handwritten-image-to-LaTeX generation , i.e. , converting handwritten mathematical expressions to LaTeX. Experimental results show that our method can significantly improve the generalization performance over standard attention-based encoder-decoder models .
In this paper , we present a novel Densely Convolutional Attention Network ( DCAN ) for SISR to improve the quality of reconstructed images . Specifically , we propose a convolutional attention mechanism which adds an weighted skip-connection joint the original convolution . Our network can adaptively recalibrate convolutional-wise features by considering interdependencies among convolutional layers .
However , models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary ( OOV ) words . In this paper , we investigate a Convolutional Attention Network called CAN for Chinese NER , which consists of a character-based convolutional neural network ( CNN ) with local-attention layer and a gated recurrent unit ( GRU ) with global self-attention layer to capture the information from adjacent characters and sentence contexts . Also , compared to other models , not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical .
Most of the articles in this special issue address the problem of recognition and representation learning . The article titled “ Convolutional Attention Networks for Scene Text Recognition , ” by Shancheng Fang , Hongtao Xie , Yating Yang , Yan Li , and Yongdong Zhang , presents a convolutional attention network ( CAN ) for unconstrained scene text recognition based on a fully Convolutional Neural Network ( CNN ) architecture and the attention mechanism . The article “ Structure-Aware Deep Learning for Product Image Classification , ” by Zhineng Chen , Shanshan Ai , and Caiyan Jia , classifies product images by leveraging the hierarchical structure and attributes existing in online web stores .
Considering this critical gap , the focus in this letter is developing an effective EEG signal analysis adaptively applied to subject-independent settings . We present a convolutional recurrent attention model ( CRAM ) that utilizes a convolutional neural network to encode the high-level representation of EEG signals and a recurrent attention mechanism to explore the temporal dynamics of the EEG signals as well as to focus on the most discriminative temporal periods . Extensive experiments on a benchmark multiclass EEG dataset containing four movement intentions indicate that the proposed model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches by at least eight percentage points .
A graph structure is first developed to represent the positioning information of EEG nodes . Then a convolutional recurrent attention model learns EEG features from both spatial and temporal dimensions and emphasizes on the most distinguishable temporal periods . We evaluate the proposed approach on two benchmark EEG datasets of motor imagery classification on the subject-independent testing .
Motivated by the finding in neuroscience that human possesses the template-searching attention mechanism , we propose to use convolution operation to simulate attentions and give a mathematical explanation of our neural attention model . We then introduce a new network architecture , which combines a recurrent neural network with our convolution-based attention model and further stacks an attention-based neural model to build a hierarchical sentiment classification model . The experimental results show that our proposed models can capture salient parts of the text to improve the performance of sentiment classification at both the sentence level and the document level .
In this paper , we design an approach for recognizing styles in photographs by introducing adapted deep convolutional neural networks that are attentive towards strong neural activations . The proposed convolutional attentional units act as a filtering mechanism that conserves activations in convolutional blocks in order to contribute more meaningfully towards the visual style classes . State-of-the-art results were achieved on two large image style datasets , demonstrating the effectiveness of our method .
For spatial attention , we learn a saliency mask to allow the model to focus on the most salient parts of the feature maps . For temporal attention , we employ a convolutional LSTM based attention mechanism to identify the most relevant frames from an input video . Further , we propose a set of regularizers to ensure that our attention mechanism attends to coherent regions in space and time .
The first part of this thesis discusses the application of artificial intelligence to stock price prediction and the building of another artificial intelligence model for stock price prediction during mergers and acquisitions . It is demonstrated that a combination of Long Short-Term Memory/Recurrent Neural Network + Convolutional Neural Network + Attention ( LSTM/RNN+CNN+Attention ) trained on dailyand minute-stock price and volume data are able to predict future stock prices and stock price changes three times better than traditional non-deep learning methods . Further research has been done on stock price prediction during Mergers and Acquisitions for technology unicorns , finding that when more information on M&A has been added into the input dataset , the accuracy can be improved by about 2 % .
further tuned the ABS model with additional features to balance the abstractive and extractive tendency . As the extension of the ABS model , it used a convolutional attention - based encoder and an RNN decoder . used a full s2s RNN model and added the hand - crafted features such as POS tag and NER , to enhance the encoder representation .
- lvt5k - 1sent outperforms the state - of - the - art model of with statistically significant improvement on Rouge - 1 . We believe the bidirectional RNN we used to model the source captures richer contextual information of every word than the bag - of - embeddings representation used by namas and chopra in their convolutional attentional encoders , which might explain our superior performance . Further , explicit modeling of important information such as multiple source sentences , word - level linguistic features , using the switch mechanism to point to source words when needed , and hierarchical attention , solve specific problems in summarization , each boosting performance incrementally .
To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns , we design the localization branch , which aims to coarsely localize the target to help the regression branch to generate accurate results . Meanwhile , we introduce the global context module into the localization branch to capture long-range dependency for more robustness in large displacement of target . In addition , we design a multi-scale learnable attention module to guide these three branches to exploit discriminative features for better performance .
Its performance is on par with the most accurate , time-consuming online-learning model , while its speed is similar to the fastest template-matching method which has sub-optimal accuracy . The core in achieving this is a novel global context module that reliably summarizes and propagates information through the entire video . Compared to previous approaches that only use the first , the last , or a select few frames to guide the segmentation of the current frame , the global context module allows us to use all past frames to guide the processing .
For region proposal we propose a novel lightweight cascade structure which can effectively improve RPN proposal quality . For object recognition we re-implement global context modeling with a few modications and obtain a performance boost ( 4.2 % mAP gain on the ILSVRC 2016 validation set ) . Besides , we apply the idea of pre-training extensively and show its importance in both steps .
In this paper , a novel Mask R-CNN approach is proposed . Firstly , we combine global context modeling with Mask R-CNN by capturing long-range dependencies . Secondly , we extend the Mask R-CNN approach by fusing a new simplified non-local neural network and Squeeze-and-Excitation block , which helps to achieve long-range dependencies and adaptively recalibrate the channel feature response by modeling the interdependence between channels .
As the names are often misspelled or abbreviated , the conventional techniques have failed to detect the noisy names properly . In this paper we propose a global context based framework for handling the noisy names . The framework is tested on a named entity recognition system designed to identify the names from the discussion texts in a homeopathy diagnosis discussion forum .
The algorithm for extracting straight lines should have characteristics of high computing speed and high accuracy . The results sho w that the str aight lines extraction algorithm with global context analysis and parametric con trol is very effective and can be used in many applications .
The object graph comprises entity , property and relation nodes . We have developed a global context aware algorithm to determine the ontological types of these nodes . Type of one node can help determine the types of other related nodes .
We propose a context-aware neural network model for temporal information extraction , with a uniform architecture for event-event , event-timex and timex-timex pairs . A Global Context Layer ( GCL ) , inspired by the Neural Turing Machine ( NTM ) , stores processed temporal relations in the narrative order , and retrieves them for use when the relevant entities are encountered . Relations are then classified in this larger context .
The purpose of this work is to devise a neural network with global context feature information for accomplishing medical image segmentation of different tasks . METHODS The proposed global context network ( GC-Net ) consists of two components ; feature encoding and decoding modules . We use multiple convolutions and batch normalization layers in the encoding module .
DeepLab is an algorithm based on the fully convolutional networks . However , DeepLab algorithm still has room for improvement , and we design three improved methods : ( 1 ) the global context structure module , ( 2 ) highly efficient decoder module , and ( 3 ) multi-scale feature fusion module . The experimental results show that the three improved methods that we proposed in this paper can make the model obtain more expressive features and improve the accuracy of the algorithm .
Meanwhile , we introduce a competition mechanism to handle distortions , in which we build a specialized graph to formulate and handle tracking under distortion as a maximum multi clique problem . Furthermore , a global-local context model is exploited to alleviate underlying distortions during the tracking process . Extensive experiments on the Online Tracking Benchmark show that our tracker can find the optimal target trajectory during the distortion period and retrieve the possibly missing target , consequently outperforms the state-of-the-art methods and improves the performance of CF-based trackers favorably .
The example underlying the proof gives a clear illustration of the label bias problem . paragraph : Global Models can be Strictly More Expressive than Local Models Consider a tagging problem where the task is to map an input sequence to a decision sequence .
dense - no - i m is the same as dense but takes only the point cloud as input . global is a baseline model that directly regresses the 8 corner locations of a 3D box , as shown in Fig . [ reference ]
This baseline result is close to the 2nd - place winner ’s ensemble result ( 25.1% / 45.8 % by FAIRCNN ) . On our baseline result , we further adopt global context modeling and multi - scale testing as in , and ensembling . Our final result on the test - challenge set is 28.2% / 51.5 % , which won the 1st place in the COCO segmentation track of ILSVRC & COCO 2015 competitions .
It is followed by a convolution layer to generate the final prediction map in ( d ) . To explain our structure , PSPNet provides an effective global contextual prior for pixel - level scene parsing . The pyramid pooling module can collect levels of information , more representative than global pooling .
The application of scSE can improve the adaptability of the feature channel and aggregate relevant spatial information , which greatly reduce false detection and missed detection . In addition , during the final segmentation phase , a spatial Squeeze-and-Excitation module ( sSE ) is used to refine the segmentation . The experimental result reveals that , both detection and instance segmentation have achieved significant improvements in the MS COCO dataset .
The Camera Calibration and 3D Reconstruction Based on a Special Object Image modeling system development for robust descriptor of environmental change Methodologies For Understanding And Evaluation Of Image Processing Algorithms A Semi-Automation of a Cost Benefit Analysis Method Counting Dense Objects in Remote Sensing Images CBAM: Convolutional Block Attention Module A new Approach towards Cost and Benefit Enterprise Architecture Analysis Cégeps en réseau Quantifying the value of architecture design decisions: lessons from the field A Study on the Stages of Concerns of the Backbone English Teachers in the Implementation of New Curriculum Based on the Theory of CBAM From requirements negotiation to software architecture decisions Experience of combining qualitative and quantitative analysis methods for evaluating software architecture MEDA: Multi-output Encoder-Decoder for Spatial Attention in Convolutional Neural Networks Context propagation embedding network for weakly supervised semantic segmentation Image-to-Markup Generation via Paired Adversarial Learning Densely convolutional attention network for image super-resolution CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition Editorial to Special Issue on Deep Learning for Intelligent Multimedia Analytics A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis Motor Imagery Classification via Temporal Attention Cues of Graph Embedded EEG Signals Convolution-Based Neural Attention With Applications to Sentiment Classification Paying Attention to Style: Recognizing Photo Styles with Convolutional Attentional Units Interpretable Spatio-Temporal Attention for Video Action Recognition Machine learning applications in finance: some case studies Faithful to the Original: Fact Aware Neural Abstractive Summarization Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond SiamMan: Siamese Motion-aware Network for Visual Tracking Fast Video Object Segmentation using the Global Context Module Cascade Region Proposal and Global Context for Deep Object Detection An Improved Mask R-CNN with Global Context Modeling for Instance Segmentation Use of global context for handling noisy names in discussion texts of a homeopathy discussion forum A fast extracting straight lines algorithm with parame tric control Context Aware Ontology based Information Extraction Context-Aware Neural Model for Temporal Information Extraction GC-Net: Global context network for medical image segmentation Image semantic segmentation with an improved fully convolutional network Distortion-Aware Correlation Tracking Globally Normalized Transition-Based Neural Networks PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation Instance-Aware Semantic Segmentation via Multi-task Network Cascades Pyramid Scene Parsing Network More Competitive Feature Extraction Network for Instance Segmentation
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FCV ICASSP ECCV (7) ICSE ACIS-ICIS ECML/PKDD (1) ACCV Workshops AAAI CoNLL COMAD ACL (1) ACL 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SmartIoT
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