Search Results for author: Zhiwei Yang

Found 22 papers, 11 papers with code

HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition

no code implementations Findings (EMNLP) 2021 Zhiwei Yang, Jing Ma, Hechang Chen, Yunke Zhang, Yi Chang

Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network.

named-entity-recognition Named Entity Recognition +3

Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom

1 code implementation6 May 2024 Bo wang, Jing Ma, Hongzhan Lin, Zhiwei Yang, Ruichao Yang, Yuan Tian, Yi Chang

To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework.

Fake News Detection Language Modelling +1

Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection

no code implementations12 Apr 2024 Zhiwei Yang, Jing Liu, Peng Wu

Further, we propose a learnable text prompt mechanism with the assist of a normality visual prompt to further improve the matching accuracy of video event description text and video frames.

Anomaly Detection Domain Adaptation +2

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom

1 code implementation25 Oct 2023 Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang

This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences.

Misinformation Multiple Instance Learning +2

OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification

1 code implementation11 Jul 2023 Linhao Qu, Yingfan Ma, Zhiwei Yang, Manning Wang, Zhijian Song

In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples.

Active Learning Image Classification

SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification

no code implementations30 Apr 2023 Minghui Yang, Jing Liu, Zhiwei Yang, Zhaoyang Wu

Focusing on more effective and comprehensive anomaly detection, we propose a network based on self-supervised learning and self-attentive graph convolution (SLSG) for anomaly detection.

Classification One-Class Classification +1

Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification

no code implementations ICCV 2023 Linhao Qu, Zhiwei Yang, Minghong Duan, Yingfan Ma, Shuo Wang, Manning Wang, Zhijian Song

However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications.

Image Classification Multiple Instance Learning

A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

1 code implementation COLING 2022 Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, Yi Chang

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances.

Fake News Detection

Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection

1 code implementation22 Jul 2022 Zhiwei Yang, Peng Wu, Jing Liu, Xiaotao Liu

Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space.

Anomaly Detection

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