Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Videos
Videos
Thesis submission
Thesis submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Contact us
Contact us
IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES
Date
2018-10-10
Author
Kaya, Emre Can
Alatan, Abdullah Aydın
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
10
views
0
downloads
Cite This
A novel extension to proposal-based detection is proposed in order to learn convolutional context features for determining boundaries of objects better. Objects and their context are aimed to be learned through parallel convolutional stages. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection benchmark dataset and yielded improvements in performance over baseline, for all classes, especially the ones with distinctive context.
Subject Keywords
CNN
,
Region Proposal Network
,
Object detection
,
Context
,
Deep learning
URI
https://hdl.handle.net/11511/53076
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. C. Kaya and A. A. Alatan, “IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53076.