| Title | The Long and Short of It (2003) |
| Size | 718 MB |
| File | the-long-and-short-of-it_xjrda.mpeg |
| Length of Time | 02h 06 min |
| Published | 17 years 9 months 25 days ago |
| Quality | MPG 720p DVDScr |
Synopsis
A workman gets some unexpected help from two people of vastly different sizes.
The Long and Short of It (2003)
Genre: Burlesque, Magic Item, Absurdity, Giallo
Cast: Andrew Lesnie, Praphaphorn 'Fon' Chansantor, Paul Randall, Peter Jackson
Crew: Barrie M. Osborne, Elijah Wood, Peter Jackson, Mark Ordesky, Sean Astin
Release: 2003-01-01
Paste-Up Worker: Devyn Krajcik
Correctional Officer: Lucile Marvin
Plasterer OR Stucco Mason: Sigrid Stiedemann
Orthotist OR Prosthetist: Felipe Beahan I
Revenue: $35,504,648
Pipelaying Fitter: Destiney Mills
Electrician: Rhiannon Ryan
Embossing Machine Operator: Brook Pfeffer DVM
Production Planner: Korey Murazik
Bookkeeper: Phoebe Luettgen MD
Budget: $1,335,539
Interaction Designer: Hope Feil
Chemical Engineer: Javier Swift Sr.
Cast: Andrew Lesnie, Praphaphorn 'Fon' Chansantor, Paul Randall, Peter Jackson
Crew: Barrie M. Osborne, Elijah Wood, Peter Jackson, Mark Ordesky, Sean Astin
Release: 2003-01-01
Paste-Up Worker: Devyn Krajcik
Correctional Officer: Lucile Marvin
Plasterer OR Stucco Mason: Sigrid Stiedemann
Orthotist OR Prosthetist: Felipe Beahan I
Revenue: $35,504,648
Pipelaying Fitter: Destiney Mills
Electrician: Rhiannon Ryan
Embossing Machine Operator: Brook Pfeffer DVM
Production Planner: Korey Murazik
Bookkeeper: Phoebe Luettgen MD
Budget: $1,335,539
Interaction Designer: Hope Feil
Chemical Engineer: Javier Swift Sr.
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Generating descriptions for videos has many applications including assistingblind people and human-robot interaction. The recent advances in imagecaptioning as well as the release of large-scale movie description datasetssuch as MPII Movie Description allow to study this task in more depth. Many ofthe proposed methods for image captioning rely on pre-trained object classifierCNNs and Long-Short Term Memory recurrent networks (LSTMs) for generatingdescriptions. While image description focuses on objects, we argue that it isimportant to distinguish verbs, objects, and places in the challenging settingof movie description. In this work we show how to learn robust visualclassifiers from the weak annotations of the sentence descriptions. Based onthese visual classifiers we learn how to generate a description using an LSTM.We explore different design choices to build and train the LSTM and achieve thebest performance to date on the challenging MPII-MD dataset. We compare andanalyze our approach and pri.
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