activity
20172020
most citedFocus Your Attention: A Bidirectional Focal Attention Network for Image-Text Matching

18 citations · 37 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2020

Focus-Constrained Attention Mechanism for CVAE-based Response Generation

Zhi Cui, Yanran Li, Jiayi Zhang +3

To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models. The latent variable is supposed to capture the discourse-level…

cs.CL20201 cited

Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering

Xin Cong, Bowen Yu, Tingwen Liu +3

Few-shot classification tends to struggle when it needs to adapt to diverse domains. Due to the non-overlapping label space between domains, the performance of conventional domain…

cs.SI20205 cited

Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter

Qi Huang, Junshuai Yu, Jia Wu +1

With the rapid development of mobile Internet technology and the widespread use of mobile devices, it becomes much easier for people to express their opinions on social media. The…

cs.CL202013 cited

Unified Multi-Criteria Chinese Word Segmentation with BERT

Zhen Ke, Liang Shi, Erli Meng +3

Multi-Criteria Chinese Word Segmentation (MCCWS) aims at finding word boundaries in a Chinese sentence composed of continuous characters while multiple segmentation criteria exist.…

cs.MM201918 cited

Focus Your Attention: A Bidirectional Focal Attention Network for Image-Text Matching

Chunxiao Liu, Zhendong Mao, An-An Liu +3

Learning semantic correspondence between image and text is significant as it bridges the semantic gap between vision and language. The key challenge is to accurately find and corre…

cs.CL2017

Learning neural trans-dimensional random field language models with noise-contrastive estimation

Bin Wang, Zhijian Ou

Trans-dimensional random field language models (TRF LMs) where sentences are modeled as a collection of random fields, have shown close performance with LSTM LMs in speech recognit…