Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Lluis Castrejon"'
Publikováno v:
ICCV
Predicting future frames for a video sequence is a challenging generative modeling task. Promising approaches include probabilistic latent variable models such as the Variational Auto-Encoder. While VAEs can handle uncertainty and model multiple poss
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::369db80214a47af17be31d69dfb2484f
Publikováno v:
other univ website
People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal sc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37dc47f776b19cfed86294c43c9e2834
https://pubmed.ncbi.nlm.nih.gov/28922114
https://pubmed.ncbi.nlm.nih.gov/28922114
Publikováno v:
CVPR
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4feb56c70bbb35bf0b167df2cf9365d3
Publikováno v:
CVPR
There is growing interest in artificial intelligence to build socially intelligent robots. This requires machines to have the ability to "read" people's emotions, motivations, and other factors that affect behavior. Towards this goal, we introduce a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5685217b14c64c3d7deb555d140a1e7c
Publikováno v:
arXiv
CVPR
CVPR
People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal sc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e60c7e1c5997663a36311603289ac515
http://arxiv.org/abs/1607.07295
http://arxiv.org/abs/1607.07295