Abstract
dc:description.abstractStatic image action recognition aims at identifying the action performed in a given image. Most existing static image action recognition approaches use high-level cues present in the image such as objects, object human interaction, or human pose to better capture the action performed. Unlike images, videos have temporal information that greatly improves action recognition by resolving potential ambiguity. We propose to leverage a large amount of readily available unlabeled videos to transfer the temporal information from video domain to static image domain and hence improve static image action recognition. Specifically, We propose a video prediction model to predict the future video of a static image and use the future predicted video to improve static image action recognition. Our experimental results on four datasets validate that the idea of transferring the temporal information from videos to static images is promising, and can enhance static image action recognition performance.
Degree
thesis:*- Name thesis:degree_name
- Master of Science
- Level thesis:degree_level
- masters
- Discipline thesis:degree_discipline
- Computer Engineering
- Department dc:contributor.department
- Electrical and Computer Engineering
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2018
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- AlBahar, Badour A Sh A.
- Chair dc:contributor.committeechair
-
- Huang, Jia-Bin
- Committee members dc:contributor.committeemember
-
- Tokekar, Pratap
- Abbott, A. Lynn
Subjects
dc:subject × 4Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:15485
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/83602