Search Results for author:
Found 187 papers, 57 papers with code
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition
Second, the referents may be far from representing corresponding entity classes due to the label scarcity in the few-shot setting.
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.
NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance
In this work, we propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces.
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks
The Optimal Power Flow (OPF) problem is pivotal for power system operations, guiding generator output and power distribution to meet demand at minimized costs, while adhering to physical and engineering constraints.
Vision-Language Instruction Tuning: A Review and Analysis
Instruction tuning is a crucial supervised training phase in Large Language Models (LLMs), aiming to enhance the LLM's ability to generalize instruction execution and adapt to user preferences.
FP8-LM: Training FP8 Large Language Models
In this paper, we explore FP8 low-bit data formats for efficient training of large language models (LLMs).
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token.
Making LLaMA SEE and Draw with SEED Tokenizer
We identify two crucial design principles: (1) Image tokens should be independent of 2D physical patch positions and instead be produced with a 1D causal dependency, exhibiting intrinsic interdependence that aligns with the left-to-right autoregressive prediction mechanism in LLMs.
One For All: Video Conversation is Feasible Without Video Instruction Tuning
Without bells and whistles, BT-Adapter achieves (1) state-of-the-art zero-shot results on various video tasks using thousands of fewer GPU hours.
Ranked #2 on
Zero-Shot Video Retrieval
on ActivityNet
(using extra training data)
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+6
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions.
A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training
The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers.
GHuNeRF: Generalizable Human NeRF from a Monocular Video
In view of these limitations, we propose GHuNeRF to learn a generalizable human NeRF model from a monocular video of the human performer.
PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage
This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage.
Calibrating Uncertainty for Semi-Supervised Crowd Counting
A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set.
ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions
As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multiple images, including a large amount of information required for image and target detection.
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation
In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics.
DETR Doesn't Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
Weakly-supervised 3D Pose Transfer with Keypoints
The main challenges of 3D pose transfer are: 1) Lack of paired training data with different characters performing the same pose; 2) Disentangling pose and shape information from the target mesh; 3) Difficulty in applying to meshes with different topologies.
Confidence Estimation Using Unlabeled Data
We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process.
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently.
Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development.
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts
We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.
OD-NeRF: Efficient Training of On-the-Fly Dynamic Neural Radiance Fields
Dynamic neural radiance fields (dynamic NeRFs) have demonstrated impressive results in novel view synthesis on 3D dynamic scenes.
a unified front-end framework for english text-to-speech synthesis
The front-end is a critical component of English text-to-speech (TTS) systems, responsible for extracting linguistic features that are essential for a text-to-speech model to synthesize speech, such as prosodies and phonemes.
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing
NL-CS Net is composed of the up-sampling module and the recovery module.
Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Renyi's Entropy Perspective
However, the current generalization error bounds within this framework are still far from optimal, while substantial improvements on these bounds are quite challenging due to the intractability of high-dimensional information quantities.
Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.
Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes.
An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System
Fault diagnosis is a crucial area of research in industry.
TagGPT: Large Language Models are Zero-shot Multimodal Taggers
Given a new entity that needs tagging for distribution, TagGPT introduces two alternative options for zero-shot tagging, i. e., a generative method with late semantic matching with the tag set, and another selective method with early matching in prompts.
ScarceNet: Animal Pose Estimation with Scarce Annotations
To this end, we propose the ScarceNet, a pseudo label-based approach to generate artificial labels for the unlabeled images.
NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects
We evaluate our model based on the novel view synthesis quality with a self-collected dataset of different moving specular objects in realistic environments.
Distribution-restrained Softmax Loss for the Model Robustness
Recently, the robustness of deep learning models has received widespread attention, and various methods for improving model robustness have been proposed, including adversarial training, model architecture modification, design of loss functions, certified defenses, and so on.
Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence
In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Efficient Diffusion Training via Min-SNR Weighting Strategy
Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence.
Ranked #7 on
Image Generation
on ImageNet 256x256
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk.
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well.
Ranked #6 on
Monocular Depth Estimation
on NYU-Depth V2
DETR Does Not Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence
In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Weak-Shot Object Detection Through Mutual Knowledge Transfer
We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset.
Incremental Neural Implicit Representation with Uncertainty-Filtered Knowledge Distillation
In view of this, we explore the task of incremental learning for NIRs in this work.
EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg).
Robust and Fast Measure of Information via Low-rank Representation
The matrix-based R\'enyi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution.
A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images
Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality.
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences.
Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals
Edge caching plays an increasingly important role in boosting user content retrieval performance while reducing redundant network traffic.
Quasi-supervised Learning for Super-resolution PET
In this paper, we propose a quasi-supervised learning method, which is a new type of weakly-supervised learning methods, to recover HR PET images from LR counterparts by leveraging similarity between unpaired LR and HR image patches.
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Being equipped with three modules (i. e., global user behavior encoder, local multi-channel encoder, and region-aware weighting strategy), MCMG is capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the region check-in patterns.
Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features
The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different blocks of the base model SegNet.
An Energy Activity Dataset for Smart Homes
The proposed energy activity dataset (EAD) has a high data type diversity in contrast to existing load monitoring datasets.
Artificial Neural Networks for Finger Vein Recognition: A Survey
To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.
Simulation of snakes using vertical body bending to traverse terrain with large height variation
Remarkably, even when frictional drag is low (snake-terrain kinetic friction coefficient of 0. 20), the body must push against the wedge with a pressure 5 times that from body weight to generate sufficient forward propulsion to move forward.
Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend
Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields.
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 Law
At a general computational energy model level, there is both strong dependency to both the hardware architecture (e. g. generic processors with different configuration of inner components- CPU and GPU, programmable integrated circuits - FPGA), as well as different interacting energy consumption aspects (e. g., data movement, calculation, control).
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
+3
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.
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.
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.
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
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy.
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.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
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.
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.
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.
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).
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.
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 #4 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).
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.
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.
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)
+18
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 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.
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.
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
Understanding User Resistance Strategies in Persuasive Conversations
Furthermore, we analyze the relationships between persuasion strategies and persuasion resistance strategies.
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.
Optical Character Recognition
Optical Character Recognition (OCR)
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.
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.
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.
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 #27 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.
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.
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.
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 #17 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.