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Jie Wang, Jie Yang, Jiafan He and Dongliang Peng
Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier us...
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Rui Zhang, Mingwei Yao, Zijie Qiu, Lizhuo Zhang, Wei Li and Yue Shen
Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data colle...
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Qiuhong Zhai, Wenhao Zhu, Xiaoyu Zhang and Chenyun Liu
In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, neglecting the importance of the passage side. We believe that both the query and ...
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Hyunmin Gwak, Yongho Jeong, Chanyeong Kim, Yonghak Lee, Seongmin Yang and Sunghwan Kim
The key to semi-supervised semantic segmentation is to assign the appropriate pseudo-label to the pixels of unlabeled images. Recently, various approaches to consistency-based training and the filtering of reliable pseudo-labels have shown remarkable res...
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Shangchen Ma and Chunlin Song
Drivable road segmentation aims to sense the surrounding environment to keep vehicles within safe road boundaries, which is fundamental in Advance Driver-Assistance Systems (ADASs). Existing deep learning-based supervised methods are able to achieve good...
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Jialin Zhang, Mairidan Wushouer, Gulanbaier Tuerhong and Hanfang Wang
Emotional speech synthesis is an important branch of human?computer interaction technology that aims to generate emotionally expressive and comprehensible speech based on the input text. With the rapid development of speech synthesis technology based on ...
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Yongkun Deng, Chenghao Zhang, Nan Yang and Huaming Chen
Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced...
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Xi Yu, Bing Ouyang and Jose C. Principe
Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especially f...
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Kunlin Liu, Ping Wang, Wenbo Zhou, Zhenyu Zhang, Yanhao Ge, Honggu Liu, Weiming Zhang and Nenghai Yu
Deepfake aims to swap a face of an image with someone else?s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video consistency and producing incoherent results. To address such a problem, we propos...
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