We apply multi-task learning to image classification tasks on MNIST-like datasets . MNIST dataset has been referred to as the { \em drosophila } of machine learning and has been the testbed of many learning theories . This paper presents the use of a template based method in order to make a head pose estimation . As an image classification problem the aim of this kind of techniques is to convert the input head image into a feature vector . The feature vectors of different persons taken at the same pose will serve to learn a head pose classifier . In addition , we adopt two schemes , namely ‘ Anchor Graph ’ and ‘ Regularized Nearest Points ( RNP ) ’ , to improve computational efficiency and identification rate . Experiments on several benchmark datasets show that our model obtains competitive recognition performance for image set classification . Considering these traits of CNN , we propose a simple yet effective method which leverages the strengths of CNN combined with the advantages of including contextual information , particularly designed for a small dataset . Our method consists of two main steps : first it uses the activation features of CNN trained for a patch-based classification and then it trains a separate classifier using features of overlapping patches to perform image-based classification using the contextual information . The proposed framework outperformed the state-of-the-art method for breast cancer classification . We report here results from classifying biomedical documents that were previously used in the TREC Genomics track , employing the image-based representation . Efforts are ongoing to improve image-based classification and analyze the relationships between text and images . The goal is to develop a new set of features to supplement current text-based features . Abstract This paper presents a max-margin multi-scale convolutional factor analysis ( MMCFA ) model , which explores the strongly discriminative principle of max-margin learning to improve the classification performance of multi-scale convolutional factor analysis ( CFA ) model with application to image data classification . Compared with the traditional factor analysis ( FA ) model , the CFA model can maintain the spatial correlation among the image pixels in two-dimensional space and capture the structural information from images via convolution kernels . The Hyperion radiance values , from the 196 bands , were converted into surface reflectance values using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes ( FLAASH ) model which is embedded in ENVI software . The data dimensionality reduction of Hyperion 's image bands was achieved by using MNF transformation whereas the Maximum Likelihood algorithm was used in order to perform image data classification . The results showed that an overall accuracy of 91 % was obtained from the classification of Hyperion image , while the overall accuracy resulted from the classification of Landsat 5 TM image was 81 % . CNN ( Convolutional Neural Network ) is a powerful method for image classifying tasks . CNN 's classifying capability is assessed by using large-scale image dataset such as ImageNet in many papers , but few works on CNN with small-scale dataset have been reported . The image classification is a classical problem of image processing , computer vision , and machine learning . This paper presents an analysis of the performance using Convolutional Neural Network ( CNN ) for image classifying using deep learning . MiniVGGNet is CNN architecture used in this paper to train a network for image classification , and CIFAR-10 is selected dataset used for this purpose . Vogel and Schiele used visual attributes describing scenes to characterize image regions and combined these local semantics into a global image description . Su et al . defined six groups of attributes to build intermediate - level features for image classification . Li et al . introduced the concept of an ‘ object bank ’ which enables objects to be used as attributes for scene representation . Music Artist Classification is a challenging task in Music Information Retrieval . There exist methods that are based on either signal processing features or deep learning algorithms . In this work , we pose auxiliary signal processing based tasks for a deep learning network that includes predicting the harmonic-percussive and vocal-non vocal components of audio files . We show that training on a combination of these tasks provides us with a well trained CNN with a good set of parameters ; which can then further be used for the artist classification task . We use a Multi-Task framework and a popular deep learning architecture to train the model jointly on the artists classification and the auxiliary tasks . We observe that vocal-non vocal separation along with alignment prediction proves to be a good auxiliary task for artist classification , improving the baseline by 6 % . In this paper , we explore the use of pre-trained CNN not as a classification tool but as a feature extraction tool for painting classification . We run an extensive array of experiments to identify the layers that work best with the problems of artist and style classification , and also discuss several novel representation and classification techniques using these features . Experiments for both artist and style classification tasks are performed on the challenging Painting-91 dataset , that includes 91 different painters and 13 diverse painting styles . Our method outperforms the best method in the state of the art by 14.0 % and 9.6 % on artist and style classification respectively . The k-nearest neighbor algorithm ( k-NN ) has been used to classify the music genre . Experiments show that tags are effective to characterize similarities between artists and the proposed approach outperforms the previous web mining approaches in artist genre classification with the average accuracy of 95 % , compared with 89.5 % of . The k-nearest neighbor algorithm ( k-NN ) has been used to classify the music genre . Experiments show that tags are effective to characterize similarities between artists and the proposed approach outperforms the previous web-based approaches in artist genre classification with the highest average accuracy of 95 % , compared with 89.5 % of Schedl et al. and 81.2 % of Knees et al. Two variants of the proposed method are compared with a classic k-means clustering approach and an evaluation based on folksonomy analysis is provided . The results suggest that affinity propagation is highly effective in the music domain , allowing for better classification of artists than classic clustering techniques . Furthermore , an analysis of the results indicates that classifying music by genres , even using more than one genre for each artist , is sometimes an oversimplification of the dynamics that govern the music ecosystem . The dataset consists of paintings from 91 different painters . We further show three applications of our dataset namely : artist categorization , style classification and saliency detection . We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization . For both categorization tasks , our experimental results suggest that combining multiple features significantly improves the final performance . The automatic extraction of objective features from paintings , like brushstroke distribution , orientation , and shape , could be particularly useful for different artwork analyses and management tasks . In fact , these features contribute to provide a unique signature of the artists ’ style and can be effectively used for artist identification and classification , artwork examination and retrieval , etc . In this paper , an automatic technique for unsupervised extraction of individual brushstrokes from digital reproductions of van Gogh ’s paintings is presented . First , we use the i-vectors for an unsupervised music similarity estimation , where we calculate the distance between i-vectors in order to predict the genre of songs . Second , for a supervised artist classification task we report the performance measures using multiple classifiers trained on the i-vectors . Standard datasets for each task are used to evaluate our method and the results are compared with the state of the art . To solve this problem , we ( 1 ) contribute an unstructured social activity attribute ( USAA ) dataset with both visual and audio attributes , ( 2 ) introduce the concept of semi-latent attribute space and ( 3 ) propose a novel model for learning the latent attributes which alleviate the dependence of existing models on exact and exhaustive manual specification of the attribute-space . We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multi-media sparse data learning tasks including : multi-task learning , N-shot transfer learning , learning with label noise and importantly zero-shot learning . To solve this problem , we 1 ) introduce a concept of semilatent attribute space , expressing user-defined and latent attributes in a unified framework , and 2 ) propose a novel scalable probabilistic topic model for learning multimodal semilatent attributes , which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort . We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including : multitask learning , learning with label noise , N-shot transfer learning , and importantly zero-shot learning . In label-noise learning , \textit{noise transition matrix } , denoting the probabilities that clean labels flip into noisy labels , plays a central role in building \textit{statistically consistent classifiers}. Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points } ( i.e. , data points that belong to a specific class almost surely ) . However , when there are no anchor points , the transition matrix will be poorly learned , and those current consistent classifiers will significantly degenerate . Moreover , the formulation can be addressed globally by simple convex quadratic or linear program efficiently . Experiments on multiple weakly supervised learning tasks such as label noise learning , domain adaptation and semi-supervised learning validate the effectiveness . Positive and unlabeled learning ( PU learning ) aims to train a binary classifier based on only PU data . Existing methods usually cast PU learning as a label noise learning problem or a cost-sensitive learning problem . However , none of them fully take the data distribution information into consideration when designing the model , which hinders them from acquiring more encouraging performance . A considerable amount of videos on the web is associated with rich but noisy contextual information , such as the title and other multi-modal information , which provides weak annotations or labels about the video content . To tackle the problem of large-scale noisy learning , We propose a novel method called Multi-modal WEbly-Labeled Learning ( WELL-MM ) , which is established on the state-of-the-art machine learning algorithm inspired by the learning process of human . WELL-MM introduces a novel multi-modal approach to incorporate meaningful prior knowledge called curriculum from the noisy web videos . We consider the learning from noisy labels ( NL ) problem which emerges in many real-world applications . In addition to the widely-studied synthetic noise in the NL literature , we also consider the pseudo labels in semi-supervised learning ( Semi-SL ) as a special case of NL . In this paper , we present supervised fractional-order embedding multiview canonical correlation analysis ( SFEMCCA ) . SFEMCCA is a CCA method realizing the following three points : ( 1 ) learning noisy data with small number of samples and large number of dimensions , ( 2 ) multiview learning that can integrate three or more kinds of features , and ( 3 ) supervised learning using labels corresponding to the samples . In real data , it is necessary to deal with high dimensional noisy data with limited number of samples , and there are many cases where three or more kinds of multimodal and supervised data are treated in order to calculate more accurate projections . ( 3 ) It could replace any normalization layers in a deep network and still produce stable model training . Extensive experiments demonstrate the effectiveness of EN in a wide spectrum of tasks including image recognition , noisy label learning , and semantic segmentation . For example , by replacing BN in the ordinary ResNet50 , improvement produced by EN is 300 % more than that of SN on both ImageNet and the noisy WebVision dataset . There are several challenges for AI models of higher cognitive abilities like the profusion of knowledge , different forms of reasoning , the gap between neuro-inspired approaches and conceptual representations , the problem of inconsistent data , and the manifold of computational paradigms . The I-Cog architecture - proposed as a step towards a solution for these problems - consists of a reasoning device based on analogical reasoning , a rewriting mechanism operating on the knowledge base , and a neuro-symbolic interface for robust learning from noisy data . I-Cog is intended as a framework for human-level intelligence ( HLI ) . An incremental algorithm for supervised learning of noisy data using two layers neural networks with linear output units and a mixture of sigmoids and radial basis functions in the hidden layer ( 2-S , RBF]NN ) is proposed . Each time the network has to be extended , we compare diierent estimations of the residual error : the one provided by a sigmoidal unit responding to the overall input space , and those provided by a number of RBFs responding to localized regions . Synapses in real neural circuits can take discrete values , including zero ( silent or potential ) synapses . The computational role of zero synapses in unsupervised feature learning of unlabeled noisy data is still unclear , thus it is important to understand how the sparseness of synaptic activity is shaped during learning and its relationship with receptive field formation . Here , we formulate this kind of sparse feature learning by a statistical mechanics approach . Therefore , we focused on a new photo retrieval methodology improve the efficiency in a disaster monitoring . Our methodology is consisted of a photo geocoding using GPS and magnetic sensor data , geospatial arrangement of geocoded photos , and georeference rectification based on Scale-Invariant Feature Transform . Moreover , we have confirmed that our methodology can facilitate control and browsing of geocoded photos .
We apply multi-task learning to image classification tasks on MNIST-like datasets . MNIST dataset has been referred to as the { \em drosophila } of machine learning and has been the testbed of many learning theories .
This paper presents the use of a template based method in order to make a head pose estimation . As an image classification problem the aim of this kind of techniques is to convert the input head image into a feature vector . The feature vectors of different persons taken at the same pose will serve to learn a head pose classifier .
In addition , we adopt two schemes , namely ‘ Anchor Graph ’ and ‘ Regularized Nearest Points ( RNP ) ’ , to improve computational efficiency and identification rate . Experiments on several benchmark datasets show that our model obtains competitive recognition performance for image set classification .
Considering these traits of CNN , we propose a simple yet effective method which leverages the strengths of CNN combined with the advantages of including contextual information , particularly designed for a small dataset . Our method consists of two main steps : first it uses the activation features of CNN trained for a patch-based classification and then it trains a separate classifier using features of overlapping patches to perform image-based classification using the contextual information . The proposed framework outperformed the state-of-the-art method for breast cancer classification .
We report here results from classifying biomedical documents that were previously used in the TREC Genomics track , employing the image-based representation . Efforts are ongoing to improve image-based classification and analyze the relationships between text and images . The goal is to develop a new set of features to supplement current text-based features .
Abstract This paper presents a max-margin multi-scale convolutional factor analysis ( MMCFA ) model , which explores the strongly discriminative principle of max-margin learning to improve the classification performance of multi-scale convolutional factor analysis ( CFA ) model with application to image data classification . Compared with the traditional factor analysis ( FA ) model , the CFA model can maintain the spatial correlation among the image pixels in two-dimensional space and capture the structural information from images via convolution kernels .
The Hyperion radiance values , from the 196 bands , were converted into surface reflectance values using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes ( FLAASH ) model which is embedded in ENVI software . The data dimensionality reduction of Hyperion 's image bands was achieved by using MNF transformation whereas the Maximum Likelihood algorithm was used in order to perform image data classification . The results showed that an overall accuracy of 91 % was obtained from the classification of Hyperion image , while the overall accuracy resulted from the classification of Landsat 5 TM image was 81 % .
CNN ( Convolutional Neural Network ) is a powerful method for image classifying tasks . CNN 's classifying capability is assessed by using large-scale image dataset such as ImageNet in many papers , but few works on CNN with small-scale dataset have been reported .
The image classification is a classical problem of image processing , computer vision , and machine learning . This paper presents an analysis of the performance using Convolutional Neural Network ( CNN ) for image classifying using deep learning . MiniVGGNet is CNN architecture used in this paper to train a network for image classification , and CIFAR-10 is selected dataset used for this purpose .
Vogel and Schiele used visual attributes describing scenes to characterize image regions and combined these local semantics into a global image description . Su et al . defined six groups of attributes to build intermediate - level features for image classification . Li et al . introduced the concept of an ‘ object bank ’ which enables objects to be used as attributes for scene representation .
Music Artist Classification is a challenging task in Music Information Retrieval . There exist methods that are based on either signal processing features or deep learning algorithms .
In this work , we pose auxiliary signal processing based tasks for a deep learning network that includes predicting the harmonic-percussive and vocal-non vocal components of audio files . We show that training on a combination of these tasks provides us with a well trained CNN with a good set of parameters ; which can then further be used for the artist classification task . We use a Multi-Task framework and a popular deep learning architecture to train the model jointly on the artists classification and the auxiliary tasks . We observe that vocal-non vocal separation along with alignment prediction proves to be a good auxiliary task for artist classification , improving the baseline by 6 % .
In this paper , we explore the use of pre-trained CNN not as a classification tool but as a feature extraction tool for painting classification . We run an extensive array of experiments to identify the layers that work best with the problems of artist and style classification , and also discuss several novel representation and classification techniques using these features .
Experiments for both artist and style classification tasks are performed on the challenging Painting-91 dataset , that includes 91 different painters and 13 diverse painting styles . Our method outperforms the best method in the state of the art by 14.0 % and 9.6 % on artist and style classification respectively .
The k-nearest neighbor algorithm ( k-NN ) has been used to classify the music genre . Experiments show that tags are effective to characterize similarities between artists and the proposed approach outperforms the previous web mining approaches in artist genre classification with the average accuracy of 95 % , compared with 89.5 % of .
The k-nearest neighbor algorithm ( k-NN ) has been used to classify the music genre . Experiments show that tags are effective to characterize similarities between artists and the proposed approach outperforms the previous web-based approaches in artist genre classification with the highest average accuracy of 95 % , compared with 89.5 % of Schedl et al. and 81.2 % of Knees et al.
Two variants of the proposed method are compared with a classic k-means clustering approach and an evaluation based on folksonomy analysis is provided . The results suggest that affinity propagation is highly effective in the music domain , allowing for better classification of artists than classic clustering techniques . Furthermore , an analysis of the results indicates that classifying music by genres , even using more than one genre for each artist , is sometimes an oversimplification of the dynamics that govern the music ecosystem .
The dataset consists of paintings from 91 different painters . We further show three applications of our dataset namely : artist categorization , style classification and saliency detection . We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization . For both categorization tasks , our experimental results suggest that combining multiple features significantly improves the final performance .
The automatic extraction of objective features from paintings , like brushstroke distribution , orientation , and shape , could be particularly useful for different artwork analyses and management tasks . In fact , these features contribute to provide a unique signature of the artists ’ style and can be effectively used for artist identification and classification , artwork examination and retrieval , etc . In this paper , an automatic technique for unsupervised extraction of individual brushstrokes from digital reproductions of van Gogh ’s paintings is presented .
First , we use the i-vectors for an unsupervised music similarity estimation , where we calculate the distance between i-vectors in order to predict the genre of songs . Second , for a supervised artist classification task we report the performance measures using multiple classifiers trained on the i-vectors . Standard datasets for each task are used to evaluate our method and the results are compared with the state of the art .
To solve this problem , we ( 1 ) contribute an unstructured social activity attribute ( USAA ) dataset with both visual and audio attributes , ( 2 ) introduce the concept of semi-latent attribute space and ( 3 ) propose a novel model for learning the latent attributes which alleviate the dependence of existing models on exact and exhaustive manual specification of the attribute-space . We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multi-media sparse data learning tasks including : multi-task learning , N-shot transfer learning , learning with label noise and importantly zero-shot learning .
To solve this problem , we 1 ) introduce a concept of semilatent attribute space , expressing user-defined and latent attributes in a unified framework , and 2 ) propose a novel scalable probabilistic topic model for learning multimodal semilatent attributes , which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort . We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including : multitask learning , learning with label noise , N-shot transfer learning , and importantly zero-shot learning .
In label-noise learning , \textit{noise transition matrix } , denoting the probabilities that clean labels flip into noisy labels , plays a central role in building \textit{statistically consistent classifiers}. Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points } ( i.e. , data points that belong to a specific class almost surely ) . However , when there are no anchor points , the transition matrix will be poorly learned , and those current consistent classifiers will significantly degenerate .
Moreover , the formulation can be addressed globally by simple convex quadratic or linear program efficiently . Experiments on multiple weakly supervised learning tasks such as label noise learning , domain adaptation and semi-supervised learning validate the effectiveness .
Positive and unlabeled learning ( PU learning ) aims to train a binary classifier based on only PU data . Existing methods usually cast PU learning as a label noise learning problem or a cost-sensitive learning problem . However , none of them fully take the data distribution information into consideration when designing the model , which hinders them from acquiring more encouraging performance .
A considerable amount of videos on the web is associated with rich but noisy contextual information , such as the title and other multi-modal information , which provides weak annotations or labels about the video content . To tackle the problem of large-scale noisy learning , We propose a novel method called Multi-modal WEbly-Labeled Learning ( WELL-MM ) , which is established on the state-of-the-art machine learning algorithm inspired by the learning process of human . WELL-MM introduces a novel multi-modal approach to incorporate meaningful prior knowledge called curriculum from the noisy web videos .
We consider the learning from noisy labels ( NL ) problem which emerges in many real-world applications . In addition to the widely-studied synthetic noise in the NL literature , we also consider the pseudo labels in semi-supervised learning ( Semi-SL ) as a special case of NL .
In this paper , we present supervised fractional-order embedding multiview canonical correlation analysis ( SFEMCCA ) . SFEMCCA is a CCA method realizing the following three points : ( 1 ) learning noisy data with small number of samples and large number of dimensions , ( 2 ) multiview learning that can integrate three or more kinds of features , and ( 3 ) supervised learning using labels corresponding to the samples . In real data , it is necessary to deal with high dimensional noisy data with limited number of samples , and there are many cases where three or more kinds of multimodal and supervised data are treated in order to calculate more accurate projections .
( 3 ) It could replace any normalization layers in a deep network and still produce stable model training . Extensive experiments demonstrate the effectiveness of EN in a wide spectrum of tasks including image recognition , noisy label learning , and semantic segmentation . For example , by replacing BN in the ordinary ResNet50 , improvement produced by EN is 300 % more than that of SN on both ImageNet and the noisy WebVision dataset .
There are several challenges for AI models of higher cognitive abilities like the profusion of knowledge , different forms of reasoning , the gap between neuro-inspired approaches and conceptual representations , the problem of inconsistent data , and the manifold of computational paradigms . The I-Cog architecture - proposed as a step towards a solution for these problems - consists of a reasoning device based on analogical reasoning , a rewriting mechanism operating on the knowledge base , and a neuro-symbolic interface for robust learning from noisy data . I-Cog is intended as a framework for human-level intelligence ( HLI ) .
An incremental algorithm for supervised learning of noisy data using two layers neural networks with linear output units and a mixture of sigmoids and radial basis functions in the hidden layer ( 2-S , RBF]NN ) is proposed . Each time the network has to be extended , we compare diierent estimations of the residual error : the one provided by a sigmoidal unit responding to the overall input space , and those provided by a number of RBFs responding to localized regions .
Synapses in real neural circuits can take discrete values , including zero ( silent or potential ) synapses . The computational role of zero synapses in unsupervised feature learning of unlabeled noisy data is still unclear , thus it is important to understand how the sparseness of synaptic activity is shaped during learning and its relationship with receptive field formation . Here , we formulate this kind of sparse feature learning by a statistical mechanics approach .
Therefore , we focused on a new photo retrieval methodology improve the efficiency in a disaster monitoring . Our methodology is consisted of a photo geocoding using GPS and magnetic sensor data , geospatial arrangement of geocoded photos , and georeference rectification based on Scale-Invariant Feature Transform . Moreover , we have confirmed that our methodology can facilitate control and browsing of geocoded photos .
Multi-task Learning on MNIST Image Datasets Supervised Learning for Head Pose Estimation Using SVD and Gabor Wavelets A set-level joint sparse representation for image set classification Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images Exploring a new space of features for document classification: figure clustering Max-margin multi-scale convolutional factor analysis model with application to image classification Mapping agricultural crops with EO-1 Hyperion data Convolutional neural network for industrial egg classification Improving Convolutional Neural Network (CNN) architecture (miniVGGNet) with Batch Normalization and Learning Rate Decay Factor for Image Classification Image Captioning and Visual Question Answering Based on Attributes and External Knowledge A Multi-Task Music Artist Classification Network Painting Classification Using a Pre-trained Convolutional Neural Network Deep Multibranch Neural Network for Painting Categorization A Web Mining based Method for Music Genre Classification Tag-based Artist Similarity and Genre Classification What is a "Musical World"? An affinity propagation approach Painting-91: a large scale database for computational painting categorization Computer-assisted analysis of painting brushstrokes: digital image processing for unsupervised extraction of visible features from van Gogh’s works I-Vectors for Timbre-Based Music Similarity and Music Artist Classification Attribute Learning for Understanding Unstructured Social Activity Learning Multimodal Latent Attributes Are Anchor Points Really Indispensable in Label-Noise Learning? A General Formulation for Safely Exploiting Weakly Supervised Data Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning Leveraging Multi-modal Prior Knowledge for Large-scale Concept Learning in Noisy Web Data Countering Noisy Labels By Learning From Auxiliary Clean Labels Sfemcca: Supervised Fractional-Order Embedding Multiview Canonical Correlation Analysis for Video Preference Estimation Exemplar Normalization for Learning Deep Representation I-Cog: A Computational Framework for Integrated Cognition of Higher Cognitive Abilities Combining sigmoids and radial basis functions in evolutive neural architectures The role of zero synapses in unsupervised feature learning Geocoded photo retrieval and georeference rectification for ground-based disaster monitoring
608c1043e930fc909c5f76b1d26f0c376e1f0eba c01c721bf3a0fdbef67c9c822bbdf797b44d67b5 91011602ff4af3bbf9f8e4be57e6e88653c3855c bc5c5ebcb56b67979bef7ab0fc8c0c2024a07122 815582f8ebb3e88011e7d92f545d3999792cb5ba fd11e22541fb4dc34629c45ba8549b00ff889d24 11412063dca63954c3d2711f22bcb2bdc99a8490 23f3978300ef77138eb69b0df86d6308fae220db 921fa06f5fc375a17124410d6d82855b14c23550 028f8d84736383710773c3f3708f5f440f4c7f2c 176035c67bbe4e949b88acca7068214e1a85641b 66826d01a6ef98c9adcd64919f5c6ed4a12cfeae 92b414f4d8247a8d8f169d5ee7e6853d0900b895 1cb417f0fbc09921e5766e5e3e074348c31ac9a5 adcc488f0641cffc7ced8368f6281c8753b69ce2 cc11ac6e0403f0e4c540188320203f9fee085bd9 8184ac3c43338b510e9553cc6f968b3f0f28121b 01a35804f1493a4c3d238d3e85f7841a954c8728 12b0fa5b97d3770debd541738b5140c5030e48f6 f2aa1191912c82d2eab6a3ad3355cda4d789c998 4f62d8a036f7616510909d4a19db19b735264d9c 05d2cfa9cefd48240480f66b06c4b8b1fc1a9d7a 9a8acce0c08e9d1fa762a32307602e61d7fb5a9f ea0d399d32f6d3fd4bcf95bc964a9fd62629ff78 1abe6dcfcc55c25a05ac8465a4bc0b0276f1f81c 4da30fdfd616efd1a6a6f9e03eb97a61c5264baf cf302581299c4c88a253acd1576f25888ee983ef 5b019a5f4741caef712af03683552225686e1b4a 4249ea769cb43b3688b470b314633360dff509c1 486c25ee99c7483515f4bb9680df43b6c927053a a69e61cb6555dc1694835d04e33df63f07593563
["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Environmental Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Biology","Physics"] ["Mathematics"]
2018 2008 2018 2018 2006 2019 2009 2017 2019 2018 2020 2016 2017 2009 2008 2011 2014 2014 2015 2012 2014 2019 2018 2019 2017 2019 2018 2020 2007 1996 2018 2014
ISM CASCON WHISPERS ISOCC IEEE Transactions on Pattern Analysis and Machine Intelligence ICVGIP Workshops ICIAP (1) MIRUM ISMIR ECCV (4) NeurIPS AAAI ICMR ICASSP MICAI ESANN
https://api.semanticscholar.org/608c1043e930fc909c5f76b1d26f0c376e1f0eba https://api.semanticscholar.org/c01c721bf3a0fdbef67c9c822bbdf797b44d67b5 https://api.semanticscholar.org/91011602ff4af3bbf9f8e4be57e6e88653c3855c https://api.semanticscholar.org/bc5c5ebcb56b67979bef7ab0fc8c0c2024a07122 https://api.semanticscholar.org/815582f8ebb3e88011e7d92f545d3999792cb5ba https://api.semanticscholar.org/fd11e22541fb4dc34629c45ba8549b00ff889d24 https://api.semanticscholar.org/11412063dca63954c3d2711f22bcb2bdc99a8490 https://api.semanticscholar.org/23f3978300ef77138eb69b0df86d6308fae220db https://api.semanticscholar.org/921fa06f5fc375a17124410d6d82855b14c23550 https://www.semanticscholar.org/paper/6acd385b2742f65359efb99543ebfb9a0d1b850f https://api.semanticscholar.org/028f8d84736383710773c3f3708f5f440f4c7f2c https://api.semanticscholar.org/176035c67bbe4e949b88acca7068214e1a85641b https://api.semanticscholar.org/66826d01a6ef98c9adcd64919f5c6ed4a12cfeae https://api.semanticscholar.org/92b414f4d8247a8d8f169d5ee7e6853d0900b895 https://api.semanticscholar.org/1cb417f0fbc09921e5766e5e3e074348c31ac9a5 https://api.semanticscholar.org/adcc488f0641cffc7ced8368f6281c8753b69ce2 https://api.semanticscholar.org/cc11ac6e0403f0e4c540188320203f9fee085bd9 https://api.semanticscholar.org/8184ac3c43338b510e9553cc6f968b3f0f28121b https://api.semanticscholar.org/01a35804f1493a4c3d238d3e85f7841a954c8728 https://api.semanticscholar.org/12b0fa5b97d3770debd541738b5140c5030e48f6 https://api.semanticscholar.org/f2aa1191912c82d2eab6a3ad3355cda4d789c998 https://api.semanticscholar.org/4f62d8a036f7616510909d4a19db19b735264d9c https://api.semanticscholar.org/05d2cfa9cefd48240480f66b06c4b8b1fc1a9d7a https://api.semanticscholar.org/9a8acce0c08e9d1fa762a32307602e61d7fb5a9f https://api.semanticscholar.org/ea0d399d32f6d3fd4bcf95bc964a9fd62629ff78 https://api.semanticscholar.org/1abe6dcfcc55c25a05ac8465a4bc0b0276f1f81c https://api.semanticscholar.org/4da30fdfd616efd1a6a6f9e03eb97a61c5264baf https://api.semanticscholar.org/cf302581299c4c88a253acd1576f25888ee983ef https://api.semanticscholar.org/5b019a5f4741caef712af03683552225686e1b4a https://api.semanticscholar.org/4249ea769cb43b3688b470b314633360dff509c1 https://api.semanticscholar.org/486c25ee99c7483515f4bb9680df43b6c927053a https://api.semanticscholar.org/a69e61cb6555dc1694835d04e33df63f07593563