Predicting the future by predicting the past

Simple item page
dc.contributor.advisorPirsiavash, Hamed
dc.contributor.authorRaghavan, Rahul
dc.contributor.departmentComputer Science and Electrical Engineering
dc.contributor.programComputer Science
dc.date.accessioned2021-01-29T18:12:38Z
dc.date.available2021-01-29T18:12:38Z
dc.date.issued2018-01-01
dc.description.abstractFuture prediction is an important problem in computer vision. We present a couple of intuitions about jointly predicting the future and past together and show that designing and adding novel loss terms based on these intuitions can improve the future prediction. We perform experiments with two totally different future prediction frameworks and show that both can benefit from our ideas.
dc.formatapplication:pdf
dc.genretheses
dc.identifierdoi:10.13016/m2lo3a-bz3q
dc.identifier.other11858
dc.identifier.urihttp://hdl.handle.net/11603/20727
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.sourceOriginal File Name: Raghavan_umbc_0434M_11858.pdf
dc.subjectConvolution Neural Networks
dc.subjectFuture Prediction
dc.subjectNeural Networks
dc.titlePredicting the future by predicting the past
dc.typeText
dcterms.accessRightsDistribution Rights granted to UMBC by the author.
dcterms.accessRightsAccess limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
dcterms.accessRightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Raghavan_umbc_0434M_11858.pdf
Size:
35.08 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
RaghavanRPredicting_Open.pdf
Size:
42.27 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

UMBC Theses and Dissertations
UMBC Computer Science and Electrical Engineering Department
UMBC Graduate School
UMBC Student Collection