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Found 17 papers, 6 papers with code
面向汉语作为第二语言学习的个性化语法纠错(Personalizing Grammatical Error Correction for Chinese as a Second Language)
语法纠错任务旨在通过自然语言处理技术自动检测并纠正文本中的语序、拼写等语法错误。当前许多针对汉语的语法纠错方法已取得较好的效果, 但往往忽略了学习者的个性化特征, 如二语等级、母语背景等。因此, 本文面向汉语作为第二语言的学习者, 提出个性化语法纠错, 对不同特征的学习者所犯的错误分别进行纠正, 并构建了不同领域汉语学习者的数据集进行实验。实验结果表明, 将语法纠错模型适应到学习者的各个领域后, 性能得到明显提升。
From Text to CQL: Bridging Natural Language and Corpus Search Engine
This paper presents the first text-to-CQL task that aims to automate the translation of natural language into CQL.
Cost-efficient Crowdsourcing for Span-based Sequence Labeling: Worker Selection and Data Augmentation
The SES method is designed specifically for sequence labeling tasks.
The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators.
Uncertainty-Driven Action Quality Assessment
Automatic action quality assessment (AQA) has attracted increasing attention due to its wide applications.
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling
This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and French.
EDTER: Edge Detection with Transformer
In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches.
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages.
Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation
In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss.
Transform consistency for learning with noisy labels
Previous methods such as Coteaching and JoCoR introduce two different networks to select clean samples out of the noisy ones and only use these clean ones to train the deep models.
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning
We exploit a set of data-rich source domains to learn the initialization of model parameters that facilitates fast adaptation on new resource-poor target domains.
Classification-driven Single Image Dehazing
The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e. g. image classification.
Unsupervised Part Mining for Fine-grained Image Classification
To tackle this issue, we propose a fully unsupervised part mining (UPM) approach to localize the discriminative parts without even image-level annotations, which largely improves the fine-grained classification performance.
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs).
Does Haze Removal Help CNN-based Image Classification?
Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images.
Effects of Image Degradations to CNN-based Image Classification
In this paper, we empirically study this problem for four kinds of degraded images -- hazy images, underwater images, motion-blurred images and fish-eye images.
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
This paper considers the task of thorax disease classification on chest X-ray images.
