A closer look
By T. Mulvey. 2007
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Jiashi Feng
- Stefano Soatto
- Avinash Ravichandran
- Luc Van Gool
- Daquan Zhou
- Rahul Bhotika
- Trevor Darrell
- Ming-Hsuan Yang
Citations in the corpus
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- MagicVideo: Efficient Video Generation With Latent Diffusion Models (2022, background): “Thus, to reduce the high computational cost, recent video processing models typically replace 3D convolution with a 2D convolution along the spatial dimension followed by a 1D convolution [43] along the temporal dimension (termed “2D+1D”).”
- Few-Shot Learning With Embedded Class Models and Shot-Free Meta Training (2019, background): “This setting allows for the network to learn a better embedding for an open set classification as shown in [2, 20] Unlike existing sampling methods that use episodic sampling [11, 15], we do not split the images within an episode into a meta-train set and a meta-test set.”
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- Boomtowns, Resource Dependence and Socio-economic Well-being (2011, background): “This highlights the sensitivity of local labour markets to changes in commodity prices in resource towns (see also Grambling & Freudenburg 1990; Halseth 2005).”
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