Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Chaoxi Xu"'
Publikováno v:
ACM Multimedia
As reported by respected evaluation campaigns focusing both on automated and interactive video search approaches, deep learning started to dominate the video retrieval area. However, the results are still not satisfactory for many types of search tas
This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14e5f8ee4538af6fb7a102a404e77577
http://arxiv.org/abs/2009.05381
http://arxiv.org/abs/2009.05381
Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability of pre-trained image and video convolutional neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4c8bd498d09d460ac226ac9fded0171
http://arxiv.org/abs/2004.03815
http://arxiv.org/abs/2004.03815
Retrieving unlabeled videos by textual queries, known as Ad-hoc Video Search (AVS), is a core theme in multimedia data management and retrieval. The success of AVS counts on cross-modal representation learning that encodes both query sentences and vi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7dd70d1276538021777230e5b51fc08e
Publikováno v:
ACM Multimedia
Ad-hoc video search (AVS) is an important yet challenging problem in multimedia retrieval. Different from previous concept-based methods, we propose a fully deep learning method for query representation learning. The proposed method requires no expli
Autor:
Keke Zhang, Yi Lu, Zhennan Zhao, Chaoxi Xu, Xiangjia Zhu, Yinglei Zhang, Xixi He, Wenwen He, Zongjiang Shang, Lei Cai, Jun Wu, Xianfang Rong, Dayong Ding, Xirong Li
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030322502
MICCAI (4)
MICCAI (4)
Age-related cataract is a priority eye disease, with nuclear cataract as its most common type. This paper aims for automated nuclear cataract grading based on slit-lamp photos. Different from previous efforts which rely on traditional feature extract
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bda03d79f382abe71cc42ee44e0eaed
https://doi.org/10.1007/978-3-030-32251-9_56
https://doi.org/10.1007/978-3-030-32251-9_56
Publikováno v:
ACM Multimedia
This paper describes our solution for the Hulu Content-based Video Relevance Prediction Challenge. Noting the deficiency of the original features, we propose feature re-learning to improve video relevance prediction. To generate more training instanc
Publikováno v:
CVPR
This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3699155d2db12f197bad4f770d03e561
http://arxiv.org/abs/1809.06181
http://arxiv.org/abs/1809.06181
This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods. We propose COCO-CN, a novel dataset enriching MS-COCO with manually written Chinese sentences and tags. For more effective annotation acquis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6dd1c3d49382ae134a03fe3dcf4fdea