most citedLook, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning

93 citations · 93 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CL2020

Neural Deepfake Detection with Factual Structure of Text

Wanjun Zhong, Duyu Tang, Zenan Xu +5

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing a…

cs.MM202093 cited

Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning

Ying Cheng, Ruize Wang, Zhihao Pan +2

When watching videos, the occurrence of a visual event is often accompanied by an audio event, e.g., the voice of lip motion, the music of playing instruments. There is an underlyi…

cs.CL2020

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection

Ruize Wang, Duyu Tang, Nan Duan +5

We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inje…

cs.CL2020

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

Ruize Wang, Duyu Tang, Nan Duan +6

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models whe…

cs.CL2019

Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication

Ruize Wang, Zhongyu Wei, Ying Cheng +5

Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and…