activity
20182022
most citedASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition

73 citations · 132 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV202232 cited

On Distinctive Image Captioning via Comparing and Reweighting

Jiuniu Wang, Wenjia Xu, Qingzhong Wang +1

Recent image captioning models are achieving impressive results based on popular metrics, i.e., BLEU, CIDEr, and SPICE. However, focusing on the most popular metrics that only cons…

cs.CV2022

Attribute Prototype Network for Any-Shot Learning

Wenjia Xu, Yongqin Xian, Jiuniu Wang +2

Any-shot image classification allows to recognize novel classes with only a few or even zero samples. For the task of zero-shot learning, visual attributes have been shown to play…

cs.CV202018 cited

Where is the Model Looking At?--Concentrate and Explain the Network Attention

Wenjia Xu, Jiuniu Wang, Yang Wang +3

Image classification models have achieved satisfactory performance on many datasets, sometimes even better than human. However, The model attention is unclear since the lack of int…

cs.CL20207 cited

SRQA: Synthetic Reader for Factoid Question Answering

Jiuniu Wang, Wenjia Xu, Xingyu Fu +5

The question answering system can answer questions from various fields and forms with deep neural networks, but it still lacks effective ways when facing multiple evidences. We int…

cs.CL202073 cited

ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition

Jiuniu Wang, Wenjia Xu, Xingyu Fu +2

Named Entity Recognition (NER) is a challenging task that extracts named entities from unstructured text data, including news, articles, social comments, etc. The NER system has be…

cs.CV20202 cited

Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets

Jiuniu Wang, Wenjia Xu, Qingzhong Wang +1

A wide range of image captioning models has been developed, achieving significant improvement based on popular metrics, such as BLEU, CIDEr, and SPICE. However, although the genera…