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pro vyhledávání: '"Ren Yuqiang"'
As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples,the learned models are usually biased to base classes and sensitive to the variance of novel examples. To address this issue, we propo
Externí odkaz:
http://arxiv.org/abs/2301.13411
Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categories are often misclassified to existing known classes. In this work, we
Externí odkaz:
http://arxiv.org/abs/2203.14911
Autor:
Chen, Nenglun, Pan, Xingjia, Chen, Runnan, Yang, Lei, Lin, Zhiwen, Ren, Yuqiang, Yuan, Haolei, Guo, Xiaowei, Huang, Feiyue, Wang, Wenping
We study the problem of weakly supervised grounded image captioning. That is, given an image, the goal is to automatically generate a sentence describing the context of the image with each noun word grounded to the corresponding region in the image.
Externí odkaz:
http://arxiv.org/abs/2108.01056
Autor:
Pan, Xingjia, Ren, Yuqiang, Sheng, Kekai, Dong, Weiming, Yuan, Haolei, Guo, Xiaowei, Ma, Chongyang, Xu, Changsheng
Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of
Externí odkaz:
http://arxiv.org/abs/2005.09973
Autor:
Zhiwen Lin, Xingjia Pan, Runnan Chen, Ren Yuqiang, Feiyue Huang, Haolei Yuan, Lei Yang, Xiaowei Guo, Nenglun Chen, Wenping Wang
Publikováno v:
ACM Multimedia
We study the problem of weakly supervised grounded image captioning. That is, given an image, the goal is to automatically generate a sentence describing the context of the image with each noun word grounded to the corresponding region in the image.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2ec53bc176c91d676106a70368c9bad
Autor:
Xingjia Pan, Chongyang Ma, Changsheng Xu, Weiming Dong, Kekai Sheng, Ren Yuqiang, Xiaowei Guo, Haolei Yuan
Publikováno v:
CVPR
Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::853faa5ba09e0f1f281fe95d3158cdb2