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Found 133 papers, 31 papers with code
Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning
For the correction subtask, we utilize the masked language model, the seq2seq model and the spelling check model to generate corrections based on the detection results.
Mining Error Templates for Grammatical Error Correction
We have accumulated 1, 119 error templates for Chinese GEC based on this method.
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning.
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron
Cervical cytopathology image classification is an important method to diagnose cervical cancer.
A Transistor Operations Model for Deep Learning Energy Consumption Scaling
In this paper, we develop a bottom-level Transistor Operations (TOs) method to expose the role of activation functions and neural network structure in energy consumption scaling with DL model configuration.
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning.
Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis.
Histopathological Image Classification
Image Classification
+1
Optimal Randomized Approximations for Matrix based Renyi's Entropy
The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical learning and inference tasks.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.
Subspace Nonnegative Matrix Factorization for Feature Representation
Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, which means treating all features equally.
TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments.
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology
Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images.
An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images
Experimental result shows that the proposed PID-Net has the best performance and potential for dense tiny objects counting tasks, which achieves 96. 97% counting accuracy on the dataset with 2448 yeast cell images.
Towards Domain Generalization in Object Detection
Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied.
Towards Abstractive Grounded Summarization of Podcast Transcripts
Summarization of podcast transcripts is of practical benefit to both content providers and consumers.
A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements
This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Traditional machine learning methods achieve maximum accuracy of 76. 02% and deep learning method achieves a maximum accuracy of 95. 37%.
A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis
The various works related to Computer Assisted Sperm Analysis methods in the last 30 years (since 1988) are comprehensively introduced and analysed in this survey.
A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification
Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis.
What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review
In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly.
Distribution Consistent Neural Architecture Search
We first theoretically investigate how the weight coupling problem affects the network searching performance from a parameter distribution perspective, and then propose a novel supernet training strategy with a Distribution Consistent Constraint that can provide a good measurement for the extent to which two architectures can share weights.
Computing Wasserstein-p Distance Between Images With Linear Cost
In this paper, we propose a novel algorithm to compute the Wasserstein-p distance between discrete measures by restricting the optimal transport (OT) problem on a subset.
DLFormer: Discrete Latent Transformer for Video Inpainting
Video inpainting remains a challenging problem to fill with plausible and coherent content in unknown areas in video frames despite the prevalence of data-driven methods.
Bayesian neural network priors for edge-preserving inversion
We consider Bayesian inverse problems wherein the unknown state is assumed to be a function with discontinuous structure a priori.
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation
Each type of EM contains 40 original and 40 GT images, in total 1680 EM images.
Markov subsampling based Huber Criterion
Subsampling is an important technique to tackle the computational challenges brought by big data.
Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment
Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of underlying Markov chain.
Meta Mask Correction for Nuclei Segmentation in Histopathological Image
However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain.
Coarse-to-fine Animal Pose and Shape Estimation
Most existing animal pose and shape estimation approaches reconstruct animal meshes with a parametric SMAL model.
PIMIP: An Open Source Platform for Pathology Information Management and Integration
However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information.
W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image
In order to take advantage of segmentation methods based on point annotation, further alleviate the manual workload, and make cancer diagnosis more efficient and accurate, it is necessary to develop an automatic nucleus detection algorithm, which can automatically and efficiently locate the position of the nucleus in the pathological image and extract valuable information for pathologists.
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network
To address this issue, we propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports.
A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data
In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient.
A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning
Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs).
Adverse Media Mining for KYC and ESG Compliance
Adverse media or negative news screening is crucial for the identification of such non-financial risks.
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents
Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i. e., semantic entity), while the relations in-between are largely unexplored.
Ranked #1 on
Entity Linking
on FUNSD
EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation
The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ". XML" format file.
Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works
Event Extraction bridges the gap between text and event signals.
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space
We believe that this approach of exploiting general data distribution knowledge form neural networks can be applied to a wide range of scarce data open challenges.
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models.
Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer
Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it.
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB).
A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers
In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification.
Package Recommendation with Intra- and Inter-Package Attention Networks
With the booming of online social networks in the mobile internet, an emerging recommendation scenario has played a vital role in information acquisition for user, where users are no longer recommended with a single item or item list, but a combination of heterogeneous and diverse objects (called a package, e. g., a package including news, publisher, and friends viewing the news).
Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image
Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor.
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation
It is desirable to transfer the knowledge stored in a well-trained source model onto non-annotated target domain in the absence of source data.
A Comparison for Patch-level Classification of Deep Learning Methods on Transparent Environmental Microorganism Images: from Convolutional Neural Networks to Visual Transformers
We conclude that ViT performs the worst in classifying 8 * 8 pixel patches, but it outperforms most convolutional neural networks in classifying 224 * 224 pixel patches.
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading.
Towards interpreting computer vision based on transformation invariant optimization
Interpreting how does deep neural networks (DNNs) make predictions is a vital field in artificial intelligence, which hinders wide applications of DNNs.
GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer
In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation.
A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer
In this paper, we first briefly review the development of Convolutional Neural Network and Visual Transformer in deep learning, and introduce the sources and development of conventional noises and adversarial attacks.
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers
The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.
A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches
In this review, first, we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods.
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (Zero-Shot)
on CMRC 2018
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+20
A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers
Finally, the application prospect of the analytical method in this field is discussed.
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation
Existing works circumvent this problem with pseudo labels generated from data of other easily accessible domains such as synthetic data.
A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches
In this article, we have studied the development of microorganism counting methods using digital image analysis.
NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation
To facilitate the research on this corpus, we provide results of several benchmark models.
Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network
Decision of transfer layers and trainable layers is a major task for design of the transfer convolutional neural networks (CNN).
A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis
In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms.
DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques
Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors.
A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches
This paper reviews the methods of WSI analysis based on machine learning.
A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification
The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module.
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks
EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions.
Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning
In this paper, we propose Cocktail Edge Caching, that tackles the dynamic popularity and heterogeneity through ensemble learning.
Transformers satisfy
The Propositional Satisfiability Problem (SAT), and more generally, the Constraint Satisfaction Problem (CSP), are mathematical questions defined as finding an assignment to a set of objects that satisfies a series of constraints.
Generating Sports News from Live Commentary: A Chinese Dataset for Sports Game Summarization
To deeply study this task, we present SportsSum, a Chinese sports game summarization dataset which contains 5, 428 soccer games of live commentaries and the corresponding news articles.
Automatic Summarization of Open-Domain Podcast Episodes
Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary length and start/end points.
The study of multi-peaked type-I X-ray bursts in the neutron-star low mass X-ray binary 4U 1636$-$536 with RXTE
This indicates that the double-peaked structure in the light curve of the bursts may be affected by enhanced accretion rate in the disc, or increased temperature of the neutron star.
High Energy Astrophysical Phenomena
Chunk-based Chinese Spelling Check with Global Optimization
Chinese spelling check is a challenging task due to the characteristics of the Chinese language, such as the large character set, no word boundary, and short word length.
Understanding User Resistance Strategies in Persuasive Conversations
Furthermore, we analyze the relationships between persuasion strategies and persuasion resistance strategies.
Better Highlighting: Creating Sub-Sentence Summary Highlights
Amongst the best means to summarize is highlighting.
A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.
Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses
In this paper, we propose a weakly supervised deep generative network to address the inverse problem and circumvent the need for ground truth 2D-to-3D correspondences.
Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images
Semi-supervised learning (SSL) is an effective way to utilize unlabeled data and alleviate the need for labeled data.
Graph Neural News Recommendation with Unsupervised Preference Disentanglement
Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability.
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions
In this paper, we achieve partially explainable learning model by symbolically explaining the role of activation functions (AF) under a scalable topology.
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College
Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.
Generalization Study of Quantum Neural Network
In this model, we mapped the feature data to a quantum state in Hilbert space firstly, and then implement unitary evolution on it, in the end, we can get the classification result by im-plement measurement on the quantum state.
Riemannian Proximal Policy Optimization
In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems.
Understanding Linearity of Cross-Lingual Word Embedding Mappings
The technique of Cross-Lingual Word Embedding (CLWE) plays a fundamental role in tackling Natural Language Processing challenges for low-resource languages.
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Breast cancer is one of the most common and deadliest cancers among women.
Foldover Features for Dynamic Object Behavior Description in Microscopic Videos
Behavior description is conducive to the analysis of tiny objects, similar objects, objects with weak visual information and objects with similar visual information, playing a fundamental role in the identification and classification of dynamic objects in microscopic videos.
A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation
In order to assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multi-scale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper.
Gastric histopathology image segmentation using a hierarchical conditional random field
This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance.
OpenHI2 -- Open source histopathological image platform
Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine.
Effects of annotation granularity in deep learning models for histopathological images
In classification, state-of-the-art deep learning-based classifiers perform better when trained by pixel-wise annotation dataset.
Joint Parsing and Generation for Abstractive Summarization
If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged.
Ranked #23 on
Text Summarization
on GigaWord
A hybrid text normalization system using multi-head self-attention for mandarin
In this paper, we propose a hybrid text normalization system using multi-head self-attention.
Graph Neural News Recommendation with Long-term and Short-term Interest Modeling
Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history.
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-byword matching.
Context-aware Attention Model for Coreference Resolution
Coreference resolution is an important task for gaining more complete understanding about texts by artificial intelligence.
Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research
Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research.
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure.
Viewport Proposal CNN for 360deg Video Quality Assessment
In the second stage, a viewport quality network (VQ-net) is designed to rate the VQA score for each proposed viewport, in which the saliency map of the viewport is predicted and then utilized in VQA score rating.
Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network
We argue that 3D human pose estimation from a monocular input is an inverse problem where multiple feasible solutions can exist.
Monocular 3D Human Pose Estimation
Multi-Hypotheses 3D Human Pose Estimation
Leveraging Multi-grained Sentiment Lexicon Information for Neural Sequence Models
Words are annotated in fine-grained and coarse-grained labels.
Ranked #14 on
Sentiment Analysis
on SST-5 Fine-grained classification
Modeling relation paths for knowledge base completion via joint adversarial training
By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i. e. relation classifier and source discriminator), to capture shared/similar information between them.
A Lightweight Music Texture Transfer System
In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project.
Adversarial Examples: Opportunities and Challenges
Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis.
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences.
A Hybrid System for Chinese Grammatical Error Diagnosis and Correction
In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types.
DM\_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features
This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP.
Convolutional Sequence to Sequence Model for Human Dynamics
Human motion modeling is a classic problem in computer vision and graphics.
Learning the Kernel for Classification and Regression
We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems.
Robust Keyframe-based Dense SLAM with an RGB-D Camera
In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene.
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter
This paper describes the XJSA System submission from XJTU.
Familia: An Open-Source Toolkit for Industrial Topic Modeling
Familia is an open-source toolkit for pragmatic topic modeling in industry.
Radiometric Calibration From Faces in Images
We present a method for radiometric calibration of cameras from a single image that contains a human face.
Specular Highlight Removal in Facial Images
An important practical feature of the proposed method is that the skin color model is utilized in a way that does not require color calibration of the camera.
A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge
For argumentation mining, there are several sub-tasks such as argumentation component type classification, relation classification.
Using Relevant Public Posts to Enhance News Article Summarization
A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article.
Simulating Makeup Through Physics-Based Manipulation of Intrinsic Image Layers
We present a method for simulating makeup in a face image.
Bayesian Depth-from-Defocus with Shading Constraints
We present a method that enhances the performance of depth-from-defocus (DFD) through the use of shading information.
