Near infrared-visible(NIR-VIS)face recognition is to match an NIR face image to a VIS image.The main challenges of NIR-VIS face recognition are the gap caused by cross-modality and the lack of sufficient paired NIR-VI...Near infrared-visible(NIR-VIS)face recognition is to match an NIR face image to a VIS image.The main challenges of NIR-VIS face recognition are the gap caused by cross-modality and the lack of sufficient paired NIR-VIS face images to train models.This paper focuses on the generation of paired NIR-VIS face images and proposes a dual variational generator based on ResNeSt(RS-DVG).RS-DVG can generate a large number of paired NIR-VIS face images from noise,and these generated NIR-VIS face images can be used as the training set together with the real NIR-VIS face images.In addition,a triplet loss function is introduced and a novel triplet selection method is proposed specifically for the training of the current face recognition model,which maximizes the inter-class distance and minimizes the intra-class distance in the input face images.The method proposed in this paper was evaluated on the datasets CASIA NIR-VIS 2.0 and BUAA-VisNir,and relatively good results were obtained.展开更多
Supervised models for event detection usually require large-scale human-annotated training data,especially neural models.A data augmentation technique is proposed to improve the performance of event detection by gener...Supervised models for event detection usually require large-scale human-annotated training data,especially neural models.A data augmentation technique is proposed to improve the performance of event detection by generating paraphrase sentences to enrich expressions of the original data.Specifically,based on an existing human-annotated event detection dataset,we first automatically build a paraphrase dataset and label it with a designed event annotation alignment algorithm.To alleviate possible wrong labels in the generated paraphrase dataset,a multi-instance learning(MIL)method is adopted for joint training on both the gold human-annotated data and the generated paraphrase dataset.Experimental results on a widely used dataset ACE2005 show the effectiveness of our approach.展开更多
基金National Natural Science Foundation of China(No.62006039)National Key Research and Development Program of China(No.2019YFE0190500)。
文摘Near infrared-visible(NIR-VIS)face recognition is to match an NIR face image to a VIS image.The main challenges of NIR-VIS face recognition are the gap caused by cross-modality and the lack of sufficient paired NIR-VIS face images to train models.This paper focuses on the generation of paired NIR-VIS face images and proposes a dual variational generator based on ResNeSt(RS-DVG).RS-DVG can generate a large number of paired NIR-VIS face images from noise,and these generated NIR-VIS face images can be used as the training set together with the real NIR-VIS face images.In addition,a triplet loss function is introduced and a novel triplet selection method is proposed specifically for the training of the current face recognition model,which maximizes the inter-class distance and minimizes the intra-class distance in the input face images.The method proposed in this paper was evaluated on the datasets CASIA NIR-VIS 2.0 and BUAA-VisNir,and relatively good results were obtained.
基金National Natural Science Foundation of China(No.62006039)。
文摘Supervised models for event detection usually require large-scale human-annotated training data,especially neural models.A data augmentation technique is proposed to improve the performance of event detection by generating paraphrase sentences to enrich expressions of the original data.Specifically,based on an existing human-annotated event detection dataset,we first automatically build a paraphrase dataset and label it with a designed event annotation alignment algorithm.To alleviate possible wrong labels in the generated paraphrase dataset,a multi-instance learning(MIL)method is adopted for joint training on both the gold human-annotated data and the generated paraphrase dataset.Experimental results on a widely used dataset ACE2005 show the effectiveness of our approach.
