The existing Long Short-Term Memory methods for traffic prediction have two drawbacks : they do not use the departure time through the links for traffic prediction , and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction . Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks . In this paper , we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction . We propose a variant of the Transformer network which we call Weighted Transformer that uses self - attention branches in lieu of the multi - head attention . The branches replace the multiple heads in the attention mechanism of the original Transformer network , and the model learns to combine these branches during training . This branched architecture enables the network to achieve comparable performance at a significantly lower computational cost . This imposes practical difficulties when processing long sequences . We address this issue with attention mechanism over tree . In addition , the attention mechanism can be used for matching trees ( described in Section 4 as Tree matching NTI ) that carry different sequence information . It has obtained considerable attention due to its broad applications in natural language processing . Most existing studies set up sentiment classifiers using supervised machine learning approaches , such as support vector machine ( SVM ) , convolutional neural network ( CNN ) , long short - term memory ( LSTM ) , Tree - LSTM , and attention - based methods . Despite the remarkable progress made by the previous work , we argue that sentiment analysis still remains a challenge . For these reasons speech recognition is an interesting testbed for developing new attention - based architectures capable of processing long and noisy inputs . Application of attention - based models to speech recognition is also an important step toward building fully end - to - end trainable speech recognition systems , which is an active area of research . The dominant approach is still based on hybrid systems consisting of a deep neural acoustic model , a triphone HMM model and an n - gram language model . We also introduce a modification of SpecAugment that adapts the time mask size and/or multiplicity depending on the length of the utterance , which can potentially benefit large scale tasks . By using adaptive masking , we are able to further improve the performance of the Listen , Attend and Spell model on LibriSpeech to 2.2 % WER on test-clean and 5.2 % WER on test-other . The degree of enhancement is then adjusted according to each region 's complexity . When compared to the existing linear unsharp masking , adaptive unsharp masking , and warping-based methods , the proposed algorithm produces negligible noise amplification in homogeneous regions and superior edge enhancement . Converted pixel value had density characteristics that were adapted to the output image of the dot printer , and thus the density distributions of the lungs of radiographs became comparable . In addition , an adaptive unsharp masking technique with processing parameters optimized for each of the lungs and mediastinum was applied . An ROC study for the detection of lung nodules was carried out to evaluate the performance of dot printer images . PURPOSE : An adaptive mask method for defect test is provided to detect a defect of a semiconductor device by using an image comparison method . CONSTITUTION : The first image of a tested object and the second image related to the first image are provided.(310 ) The principle lies on the definition of two adaptive filter masks which estimate the two derivatives along the normal ( n ) and tangential ( t ) directions . Both filter masks are controlled by an adaptive mask whose coefficients are tuned in accordance with the local grey level distribution . The two new filters are then applied respectively to edge and corner detection : edge detection is achieved by detecting the zero-crossing of the derivative along n. and corner detection is obtained by thresholding the amplitude of the derivative along t. Results of these detections are provided on synthetise and real-world images , and swow the robustness of the new proposed approach . We use the method to compare the performance of two widely adopted CNNs : the fully convolutional network ( FCN ) and the U-Net , in segmentation of complex morphology of mitochondria . We also compare them against an adaptive active-mask algorithm in performance . We find that both networks outperform the adaptive active-mask algorithm in robustness and sensitivity and that U-Net outperforms FCN Overall , our study provides new insights into the performance of CNNs in segmentation of fluorescence microscopy images . The paper at hand presents and evaluates a Design for Test ( DFT ) approach specifically tuned to FAST . It utilizes a special scan-chain configuration in combination with an adaptive masking scheme - the required mask data is generated by respective frequency-aware algorithms . Experimental results indicate that this combination of scan-chain configuration and output masking can achieve high reduction in X-values ( up to 95 % ) without too much loss of fault information at a reasonable amount of control overhead . A segmentation method of B ultrasonic image based on the adaptive mask filtering and level set is presented , which can more accurately detect the treatment regions from the subtraction image of B ultrasonic image obtained before and after irradiating with high intensity focused ultrasound . By anatomizing and slicing up the fresh pork irradiated , the experimental results show that the actual injury is consistent with the result of the method . In practical cases we often encounter truncated images , but they are difficult to treat properly with conventional BD algorithms , since nontruncated blurred images are assumed . The proposed method is based on simulated annealing ( SA ) , to which we introduce an adaptive masking process . After a truncated segment is cut out from the blurred image , the sharp edges surrounding the segment are removed by multiplying the initial window , whose shape is determined with the initial estimate of the point spread function ( PSF ) . This paper discusses the challenges associated with profiling-based chain diagnosis in the production test environment with limited failure buffer capacity on tester . We propose the following two pattern masking application flows to enhance diagnosis resolution in this scenario : ( 1 ) generic pattern masking and ( 2 ) adaptive pattern masking . Experimental results illustrate that pattern masking will enable profiling-based scan chain diagnosis in volume diagnosis environment . With ever growing applications of digital images , encryption of images has emerged as a highly sought after area of research . In this present paper , a novel adaptive pixel masking scheme has been introduced for image encryption . Since images undergo degradations while transmission as well as storage , an image degradation model has been designed and simulated for common types of noise and blurring effects . We explore how non-luminance stimuli properties can be organized in time domain by the temporal gain of cell activities in the antagonistic retinal channels to manifest a time ordered feature map . In this approach cells have on/off receptive field profiles and their collective local behaviors become sensitized to recency information , so that local cell synapses are configured to incorporate time-space influences for an adaptive visual mask . This paper proposes a saliency-based attention model based on pulsed cosine transform that simulates the lateral surround inhibition of neurons with similar visual features . The model can be extended to Hebbian-based neural networks . Recent studies in neuroscience reveal that the brain resolves the competition among multiple visual stimuli presented simultaneously with several mechanisms of visual attention that are key to the brain 's ability to perform cognition efficiently . One such mechanism known as saliency based selective attention simplifies complex visual tasks into characteristic features and then selectively activates particular areas of the brain based on the feature ( or semantic ) information in the input . Interestingly , we note that there is a significant similarity among underlying characteristic semantics ( like color or texture ) of images across multiple objects in real world applications . An alternative to master the described problem is to adapt this bio-inspired strategy to robotics . In this thesis I present a neuromorphic multi-chip system that is derived from a saliency-based selective attention model [ Koch and Ullman , 1985 ] . My proposed solution uses building blocks derived from the brain : emulates of neurons and synapses . Specifically , SB-LSTM and TD-CNN are utilized to extract the overall variation trends and local features of ECG , respectively . Furthermore , we add a trend attention gate ( TAG ) to SB-LSTM , meanwhile , add a feature attention mechanism ( FAM ) and a location attention mechanism ( LAM ) to TD-CNN . Thus , the effects of important trends and features at key locations in ECG can be enhanced , which is conducive to obtaining a better understanding of the fluctuation pattern of ECG . ( 2 ) How will these factors interact with dataset characteristics ? Regarding the first question , harmonious combination of location attention and content attention may be crucial to alleviate semantic mismatch problem between aspect terms and opinion words . However , location attention does not reflect the fact that critical opinion words usually come left or right of corresponding aspect terms , as implied in the target-dependent method although not well elucidated before . In this paper , we propose a deep convolutional neural network ( CNN ) with layer-wise context expansion and location-based attention , for large vocabulary speech recognition . In our model each higher layer uses information from broader contexts , along both the time and frequency dimensions , than its immediate lower layer . Retinotopically organized feature maps serve as input for two main processing routes : the ' where-pathway ' dealing with location information and the ' what-pathway ' computing the shape and attributes of objects . A location-based attention mechanism operates on an early stage of visual processing selecting a contigous region of the visual field for preferential processing . Additionally , location-based attention plays an important role for invariant object recognition controling appropriate normalization processes within the what-pathway . In addition to these promising results , word-based models are more interpretable than character models , which have to be composed into words using a separate decoding step . We analyze the encoder hidden states and the attention behavior , and show that location-aware attention naturally represents words as a single speech-word-vector , despite spanning multiple frames in the input . We finally show that the Acoustic-to-Word model also learns to segment speech into words with a mean standard deviation of 3 frames as compared with human annotated forced-alignments for the Switchboard corpus . In specific , given the spatial feature maps , we replace the previous GAP-FC layer with a spatially attentive output layer ( SAOL ) by employing a attention mask on spatial logits . The proposed location-specific attention selectively aggregates spatial logits within a target region , which leads to not only the performance improvement but also spatially interpretable outputs . Moreover , the proposed SAOL also permits to fully exploit location-specific self-supervision as well as self-distillation to enhance the generalization ability during training . The semasiological part is realized by putting together a place recognition algorithm and an object recognition one . The categorization of the different places relies on the resolution of appearance-based consistency histograms , while for the recognition of objects in the scene , a hierarchical temporal memory ( HTM ) network boosted by a saliency attentional model , is utilized . These semantic attributes are then deposited on the topological map to augment it with the belief distributions regarding the visited places , enabling thus the agent to act in an intelligent manner in human populated environments . This paper applies the saliency map of attention model on Thangka image detection for the first time . Making contrast with some detection methods , an automatic detection algorithm is proposed to segment the headdress area of buddha in Thangka portrait image . In the second kind of failure cases , the neural network sees a composition of multiple objects . Because we have not implemented any detection or attention mechanism , the networks produce distorted output . section : Implementation details However , capacity of elements is always limited , and thus disambiguation by context is only possible to a limited extent . Alternatively , a location - based attention mechanism computes the alignment from the generator state and the previous alignment only such that . For instance , Graves used the location - based attention mechanism using a Gaussian mixture model in his handwriting synthesis model . Better results were obtained with a wider window , presumably because it resembles more the training conditions when at each step the attention mechanism was seeing the whole input sequence . With the wide window , both of the networks scored about 20 % PER on the long utterances , indicating that the proposed location - aware attention mechanism can scale to sequences much longer than those in the training set with only minor modifications required at the decoding stage . section : Conclusions To correctly answer visual questions about an image , the machine needs to understand both the image and question . Recently , visual attention based models have been explored for VQA , where the attention mechanism typically produces a spatial map highlighting image regions relevant to answering the question . So far , all attention models for VQA in literature have focused on the problem of identifying ‘
The existing Long Short-Term Memory methods for traffic prediction have two drawbacks : they do not use the departure time through the links for traffic prediction , and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction . Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks . In this paper , we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction .
We propose a variant of the Transformer network which we call Weighted Transformer that uses self - attention branches in lieu of the multi - head attention . The branches replace the multiple heads in the attention mechanism of the original Transformer network , and the model learns to combine these branches during training . This branched architecture enables the network to achieve comparable performance at a significantly lower computational cost .
This imposes practical difficulties when processing long sequences . We address this issue with attention mechanism over tree . In addition , the attention mechanism can be used for matching trees ( described in Section 4 as Tree matching NTI ) that carry different sequence information .
It has obtained considerable attention due to its broad applications in natural language processing . Most existing studies set up sentiment classifiers using supervised machine learning approaches , such as support vector machine ( SVM ) , convolutional neural network ( CNN ) , long short - term memory ( LSTM ) , Tree - LSTM , and attention - based methods . Despite the remarkable progress made by the previous work , we argue that sentiment analysis still remains a challenge .
For these reasons speech recognition is an interesting testbed for developing new attention - based architectures capable of processing long and noisy inputs . Application of attention - based models to speech recognition is also an important step toward building fully end - to - end trainable speech recognition systems , which is an active area of research . The dominant approach is still based on hybrid systems consisting of a deep neural acoustic model , a triphone HMM model and an n - gram language model .
We also introduce a modification of SpecAugment that adapts the time mask size and/or multiplicity depending on the length of the utterance , which can potentially benefit large scale tasks . By using adaptive masking , we are able to further improve the performance of the Listen , Attend and Spell model on LibriSpeech to 2.2 % WER on test-clean and 5.2 % WER on test-other .
The degree of enhancement is then adjusted according to each region 's complexity . When compared to the existing linear unsharp masking , adaptive unsharp masking , and warping-based methods , the proposed algorithm produces negligible noise amplification in homogeneous regions and superior edge enhancement .
Converted pixel value had density characteristics that were adapted to the output image of the dot printer , and thus the density distributions of the lungs of radiographs became comparable . In addition , an adaptive unsharp masking technique with processing parameters optimized for each of the lungs and mediastinum was applied . An ROC study for the detection of lung nodules was carried out to evaluate the performance of dot printer images .
PURPOSE : An adaptive mask method for defect test is provided to detect a defect of a semiconductor device by using an image comparison method . CONSTITUTION : The first image of a tested object and the second image related to the first image are provided.(310 )
The principle lies on the definition of two adaptive filter masks which estimate the two derivatives along the normal ( n ) and tangential ( t ) directions . Both filter masks are controlled by an adaptive mask whose coefficients are tuned in accordance with the local grey level distribution . The two new filters are then applied respectively to edge and corner detection : edge detection is achieved by detecting the zero-crossing of the derivative along n. and corner detection is obtained by thresholding the amplitude of the derivative along t. Results of these detections are provided on synthetise and real-world images , and swow the robustness of the new proposed approach .
We use the method to compare the performance of two widely adopted CNNs : the fully convolutional network ( FCN ) and the U-Net , in segmentation of complex morphology of mitochondria . We also compare them against an adaptive active-mask algorithm in performance . We find that both networks outperform the adaptive active-mask algorithm in robustness and sensitivity and that U-Net outperforms FCN Overall , our study provides new insights into the performance of CNNs in segmentation of fluorescence microscopy images .
The paper at hand presents and evaluates a Design for Test ( DFT ) approach specifically tuned to FAST . It utilizes a special scan-chain configuration in combination with an adaptive masking scheme - the required mask data is generated by respective frequency-aware algorithms . Experimental results indicate that this combination of scan-chain configuration and output masking can achieve high reduction in X-values ( up to 95 % ) without too much loss of fault information at a reasonable amount of control overhead .
A segmentation method of B ultrasonic image based on the adaptive mask filtering and level set is presented , which can more accurately detect the treatment regions from the subtraction image of B ultrasonic image obtained before and after irradiating with high intensity focused ultrasound . By anatomizing and slicing up the fresh pork irradiated , the experimental results show that the actual injury is consistent with the result of the method .
In practical cases we often encounter truncated images , but they are difficult to treat properly with conventional BD algorithms , since nontruncated blurred images are assumed . The proposed method is based on simulated annealing ( SA ) , to which we introduce an adaptive masking process . After a truncated segment is cut out from the blurred image , the sharp edges surrounding the segment are removed by multiplying the initial window , whose shape is determined with the initial estimate of the point spread function ( PSF ) .
This paper discusses the challenges associated with profiling-based chain diagnosis in the production test environment with limited failure buffer capacity on tester . We propose the following two pattern masking application flows to enhance diagnosis resolution in this scenario : ( 1 ) generic pattern masking and ( 2 ) adaptive pattern masking . Experimental results illustrate that pattern masking will enable profiling-based scan chain diagnosis in volume diagnosis environment .
With ever growing applications of digital images , encryption of images has emerged as a highly sought after area of research . In this present paper , a novel adaptive pixel masking scheme has been introduced for image encryption . Since images undergo degradations while transmission as well as storage , an image degradation model has been designed and simulated for common types of noise and blurring effects .
We explore how non-luminance stimuli properties can be organized in time domain by the temporal gain of cell activities in the antagonistic retinal channels to manifest a time ordered feature map . In this approach cells have on/off receptive field profiles and their collective local behaviors become sensitized to recency information , so that local cell synapses are configured to incorporate time-space influences for an adaptive visual mask .
This paper proposes a saliency-based attention model based on pulsed cosine transform that simulates the lateral surround inhibition of neurons with similar visual features . The model can be extended to Hebbian-based neural networks .
Recent studies in neuroscience reveal that the brain resolves the competition among multiple visual stimuli presented simultaneously with several mechanisms of visual attention that are key to the brain 's ability to perform cognition efficiently . One such mechanism known as saliency based selective attention simplifies complex visual tasks into characteristic features and then selectively activates particular areas of the brain based on the feature ( or semantic ) information in the input . Interestingly , we note that there is a significant similarity among underlying characteristic semantics ( like color or texture ) of images across multiple objects in real world applications .
An alternative to master the described problem is to adapt this bio-inspired strategy to robotics . In this thesis I present a neuromorphic multi-chip system that is derived from a saliency-based selective attention model [ Koch and Ullman , 1985 ] . My proposed solution uses building blocks derived from the brain : emulates of neurons and synapses .
Specifically , SB-LSTM and TD-CNN are utilized to extract the overall variation trends and local features of ECG , respectively . Furthermore , we add a trend attention gate ( TAG ) to SB-LSTM , meanwhile , add a feature attention mechanism ( FAM ) and a location attention mechanism ( LAM ) to TD-CNN . Thus , the effects of important trends and features at key locations in ECG can be enhanced , which is conducive to obtaining a better understanding of the fluctuation pattern of ECG .
( 2 ) How will these factors interact with dataset characteristics ? Regarding the first question , harmonious combination of location attention and content attention may be crucial to alleviate semantic mismatch problem between aspect terms and opinion words . However , location attention does not reflect the fact that critical opinion words usually come left or right of corresponding aspect terms , as implied in the target-dependent method although not well elucidated before .
In this paper , we propose a deep convolutional neural network ( CNN ) with layer-wise context expansion and location-based attention , for large vocabulary speech recognition . In our model each higher layer uses information from broader contexts , along both the time and frequency dimensions , than its immediate lower layer .
Retinotopically organized feature maps serve as input for two main processing routes : the ' where-pathway ' dealing with location information and the ' what-pathway ' computing the shape and attributes of objects . A location-based attention mechanism operates on an early stage of visual processing selecting a contigous region of the visual field for preferential processing . Additionally , location-based attention plays an important role for invariant object recognition controling appropriate normalization processes within the what-pathway .
In addition to these promising results , word-based models are more interpretable than character models , which have to be composed into words using a separate decoding step . We analyze the encoder hidden states and the attention behavior , and show that location-aware attention naturally represents words as a single speech-word-vector , despite spanning multiple frames in the input . We finally show that the Acoustic-to-Word model also learns to segment speech into words with a mean standard deviation of 3 frames as compared with human annotated forced-alignments for the Switchboard corpus .
In specific , given the spatial feature maps , we replace the previous GAP-FC layer with a spatially attentive output layer ( SAOL ) by employing a attention mask on spatial logits . The proposed location-specific attention selectively aggregates spatial logits within a target region , which leads to not only the performance improvement but also spatially interpretable outputs . Moreover , the proposed SAOL also permits to fully exploit location-specific self-supervision as well as self-distillation to enhance the generalization ability during training .
The semasiological part is realized by putting together a place recognition algorithm and an object recognition one . The categorization of the different places relies on the resolution of appearance-based consistency histograms , while for the recognition of objects in the scene , a hierarchical temporal memory ( HTM ) network boosted by a saliency attentional model , is utilized . These semantic attributes are then deposited on the topological map to augment it with the belief distributions regarding the visited places , enabling thus the agent to act in an intelligent manner in human populated environments .
This paper applies the saliency map of attention model on Thangka image detection for the first time . Making contrast with some detection methods , an automatic detection algorithm is proposed to segment the headdress area of buddha in Thangka portrait image .
In the second kind of failure cases , the neural network sees a composition of multiple objects . Because we have not implemented any detection or attention mechanism , the networks produce distorted output . section : Implementation details
However , capacity of elements is always limited , and thus disambiguation by context is only possible to a limited extent . Alternatively , a location - based attention mechanism computes the alignment from the generator state and the previous alignment only such that . For instance , Graves used the location - based attention mechanism using a Gaussian mixture model in his handwriting synthesis model .
Better results were obtained with a wider window , presumably because it resembles more the training conditions when at each step the attention mechanism was seeing the whole input sequence . With the wide window , both of the networks scored about 20 % PER on the long utterances , indicating that the proposed location - aware attention mechanism can scale to sequences much longer than those in the training set with only minor modifications required at the decoding stage . section : Conclusions
To correctly answer visual questions about an image , the machine needs to understand both the image and question . Recently , visual attention based models have been explored for VQA , where the attention mechanism typically produces a spatial map highlighting image regions relevant to answering the question . So far , all attention models for VQA in literature have focused on the problem of identifying ‘
An LSTM-Based Method with Attention Mechanism for Travel Time Prediction Weighted Transformer Network for Machine Translation Neural Tree Indexers for Text Understanding A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification Attention-Based Models for Speech Recognition Specaugment on Large Scale Datasets Scale-based image enhancement using modified anisotropic diffusion filter [Development of an image processing scheme for chest radiographs using a dot printer]. Adaptive mask technique for defect inspection Directional second order derivatives: Application to edge and corner detection Characterizing Robustness and Sensitivity of Convolutional Neural Networks in Segmentation of Fluorescence Microscopy Images Design-for-FAST: Supporting X-tolerant compaction during Faster-than-at-Speed Test Study of methods to determine HIFU treatment regions Dynamic masking of truncated image segments for blind recovery of blurred images based on simulated annealing Enhance Profiling-Based Scan Chain Diagnosis by Pattern Masking Image Encryption using Adaptive Pixel Masking under Various Noise Attacks Adaptive response organizer network-ARO for time sensitive visual masking Pulse discrete cosine transform for saliency-based visual attention Semantic driven hierarchical learning for energy-efficient image classification Neuromorphic implementation of a saliency-based visual selective attention system An Attention-based Hybrid LSTM-CNN Model for Arrhythmias Classification Deep learning models and datasets for aspect term sentiment classification: Implementing holistic recurrent attention on target-dependent memories Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention Perceiving Complex Visual Scenes: An Oscillator Neural Network Model that Integrates Selective Attention, Perceptual Organization, and Invariant Recognition Acoustic-to-Word Recognition with Sequence-to-Sequence Models Spatially Attentive Output Layer for Image Classification Semantic maps from multiple visual cues Headdress Detection Based on Saliency Map for Thangka Portrait Image A Point Set Generation Network for 3D Object Reconstruction from a Single Image Attention-Based Models for Speech Recognition Hierarchical Question-Image Co-Attention for Visual Question Answering
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ArXiv EACL ACL NIPS ICASSP EUSIPCO ICIP DDECS Asian Test Symposium DATE IJCNN INTERSPEECH NIPS SLT MVA 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS NIPS
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