Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Cheston Tan"'
Autor:
Cheston Tan, Tomaso Poggio
Publikováno v:
PLoS ONE, Vol 11, Iss 3, p e0150980 (2016)
Faces are an important and unique class of visual stimuli, and have been of interest to neuroscientists for many years. Faces are known to elicit certain characteristic behavioral markers, collectively labeled "holistic processing", while non-face ob
Externí odkaz:
https://doaj.org/article/d85b228726c845a7afafd2557465171d
Detecting temporal extents of human actions in videos is a challenging computer vision problem that requires detailed manual supervision including frame-level labels. This expensive annotation process limits deploying action detectors to a limited nu
Externí odkaz:
http://arxiv.org/abs/1904.07774
Publikováno v:
Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing.
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198052
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9221e9a11ba00c4a74106396ca936470
https://doi.org/10.1007/978-3-031-19806-9_15
https://doi.org/10.1007/978-3-031-19806-9_15
Autor:
Po-Jang Hsieh, Joo-Hwee Lim, Joanes Grandjean, Vigneshwaran Subbaraju, Qianli Xu, Kuan Jin Lee, Cheston Tan, Jiayi Zhang, Liyuan Li
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
Scientific Reports, 10
Scientific Reports
Scientific Reports, 10
Scientific Reports
Contains fulltext : 219674.pdf (Publisher’s version ) (Open Access) Lifelog photo review is considered to enhance the recall of personal events. While a sizable body of research has explored the neural basis of autobiographical memory (AM), there i
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP).
Recent advancements in deep learning, computer vision, and embodied AI have given rise to synthetic causal reasoning video datasets. These datasets facilitate the development of AI algorithms that can reason about physical interactions between object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::caa19362e0de5699f9420dd7b8536bd7
http://arxiv.org/abs/2108.06180
http://arxiv.org/abs/2108.06180
A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation
Publikováno v:
Cerebral cortex (New York, N.Y. : 1991). 32(18)
Navigation to multiple cued reward locations has been increasingly used to study rodent learning. Though deep reinforcement learning agents have been shown to be able to learn the task, they are not biologically plausible. Biologically plausible clas
There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through interact
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a7e2619c391f1f08552e33630fc4150
The problem of task planning for artificial agents remains largely unsolved. While there has been increasing interest in data-driven approaches for the study of task planning for artificial agents, a significant remaining bottleneck is the dearth of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d4e3e488d16b4b12ab2b94c85634b6d
http://arxiv.org/abs/2010.01357
http://arxiv.org/abs/2010.01357