352 citations · 1.8k across the 40 of their papers we have counts for
10 papers · 1 filter
Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims
Qiang Sheng, Juan Cao, Xueyao Zhang +2
False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable.…
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection
Chengbo Dong, Xinru Chen, Ruohan Hu +2
As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media for…
Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning
Qiang Sheng, Xueyao Zhang, Juan Cao +1
To defend against fake news, researchers have developed various methods based on texts. These methods can be grouped as 1) pattern-based methods, which focus on shared patterns amo…
Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues
Peng Qi, Juan Cao, Xirong Li +7
Recently, fake news with text and images have achieved more effective diffusion than text-only fake news, raising a severe issue of multimodal fake news detection. Current studies…
Learning to Disentangle GAN Fingerprint for Fake Image Attribution
Tianyun Yang, Juan Cao, Qiang Sheng +4
Rapid pace of generative models has brought about new threats to visual forensics such as malicious personation and digital copyright infringement, which promotes works on fake ima…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang +5
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem.…
Image Manipulation Detection by Multi-View Multi-Scale Supervision
Xinru Chen, Chengbo Dong, Jiaqi Ji +2
The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms…
Progressive Domain Expansion Network for Single Domain Generalization
Lei Li, Ke Gao, Juan Cao +6
Single domain generalization is a challenging case of model generalization, where the models are trained on a single domain and tested on other unseen domains. A promising solution…
Combat Data Shift in Few-shot Learning with Knowledge Graph
Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang +4
Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learn…