Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Erkut Erdem"'
Autor:
Mert Kilickaya, Burak Kerim Akkus, Ruket Cakici, Aykut Erdem, Erkut Erdem, Nazli Ikizler‐Cinbis
Publikováno v:
IET Computer Vision, Vol 11, Iss 6, Pp 398-406 (2017)
In the past few years, automatically generating descriptions for images has attracted a lot of attention in computer vision and natural language processing research. Among the existing approaches, data‐driven methods have been proven to be highly e
Externí odkaz:
https://doaj.org/article/d6956dcf808e4f44b78a6d469c051ca2
Publikováno v:
Multimedia Tools and Applications. 81:17457-17482
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1815ae10f64b8e772f89cf90b97937c4
https://doi.org/10.1007/s11042-022-12442-w
https://doi.org/10.1007/s11042-022-12442-w
Publikováno v:
Proceedings of The 17th International Symposium on Computer and Information Sciences ISBN: 9780429332821
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2599085bc077d1f9e159bd249027f673
https://doi.org/10.1201/9780429332821-48
https://doi.org/10.1201/9780429332821-48
Publikováno v:
Pattern Recognition Letters. 146:70-76
Collecting textual descriptions is an especially costly task for dense video captioning, since each event in the video needs to be annotated separately and a long descriptive paragraph needs to be provided. In this paper, we investigate a way to miti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cbb53b1f0ca3454aa71e554386cf18d
https://doi.org/10.1016/j.patrec.2021.02.009
https://doi.org/10.1016/j.patrec.2021.02.009
Autor:
Erkut Erdem, Aykut Erdem, Menekse Kuyu, Lucia Specia, Ozan Caglayan, Pranava Madhyastha, Begum Citamak
Publikováno v:
Machine Translation
Automatic generation of video descriptions in natural language, also called video captioning, aims to understand the visual content of the video and produce a natural language sentence depicting the objects and actions in the scene. This challenging
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc780e09cff4b9a107030a54e763ec1b
https://doi.org/10.1007/s10590-021-09276-y
https://doi.org/10.1007/s10590-021-09276-y
Autor:
Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, Deniz Yuret
Publikováno v:
Findings of the Association for Computational Linguistics
Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f5a211efe78296317e1312610117d2d
http://arxiv.org/abs/2012.04293
http://arxiv.org/abs/2012.04293
Today, the cutting edge of computer vision research greatly depends on the availability of large datasets, which are critical for effectively training and testing new methods. Manually annotating visual data, however, is not only a labor-intensive pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::747d9c0ba55d9a6a52fad3c0113011ad
https://doi.org/10.1111/cgf.14271
https://doi.org/10.1111/cgf.14271
Publikováno v:
MVA
Proceedings of MVA 2021-17th International Conference on Machine Vision Applications
Proceedings of MVA 2021-17th International Conference on Machine Vision Applications
Virtual and augmented reality (VR/AR) systems dramatically gained in popularity with various application areas such as gaming, social media, and communication. It is therefore a crucial task to have the knowhow to efficiently utilize, store or delive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c13f65ee12383da479f9b12d147ccc4c
https://doi.org/10.23919/mva51890.2021.9511406
https://doi.org/10.23919/mva51890.2021.9511406
Publikováno v:
ACM Transactions on Graphics. 39:1-17
In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37d49e9ad26e80184a359963d4f909b3
https://doi.org/10.1145/3368312
https://doi.org/10.1145/3368312
Publikováno v:
IEEE Transactions on Cognitive and Developmental Systems
Predicting saliency in videos is a challenging problem due to complex modeling of interactions between spatial and temporal information, especially when ever-changing, dynamic nature of videos is considered. Recently, researchers have proposed large-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2538744c45acaf8bf1a3b96cbf2438a6
http://arxiv.org/abs/2102.07682
http://arxiv.org/abs/2102.07682