Abstract
Generating descriptions for visual data (images and video) automatically has been a complicated task in the field of Computer Vision and Artificial Intelligence. This paper discusses the working of and improvements on an algorithm called Neural Image Captioner (NIC) by Oriol Vinyals and his team, which uses a deep convolutional and recurrent architecture to generate natural language sentences to describe the visual data input. We look at the possibility of making this algorithm train faster without allowing it to lose accuracy via the usage of techniques like Stochastic Gradient Descent and also employ an algorithm to find the perfect depth of the convolutional part of the network for different datasets. A drop of 33% was observed in the number of iterations required to get the algorithm to its original proficiency as claimed by Oriol et al.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 110-115 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781467366700 |
| DOIs | |
| Publication status | Published - 09-06-2016 |
| Event | 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015 - Trivandrum, Kerala, India Duration: 10-12-2015 → 12-12-2015 |
Conference
| Conference | 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015 |
|---|---|
| Country | India |
| City | Trivandrum, Kerala |
| Period | 10-12-15 → 12-12-15 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
Cite this
}
Natural language image descriptor. / Kishore, Anurag; Singh, Sanjay.
2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 110-115 7488398.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Natural language image descriptor
AU - Kishore, Anurag
AU - Singh, Sanjay
PY - 2016/6/9
Y1 - 2016/6/9
N2 - Generating descriptions for visual data (images and video) automatically has been a complicated task in the field of Computer Vision and Artificial Intelligence. This paper discusses the working of and improvements on an algorithm called Neural Image Captioner (NIC) by Oriol Vinyals and his team, which uses a deep convolutional and recurrent architecture to generate natural language sentences to describe the visual data input. We look at the possibility of making this algorithm train faster without allowing it to lose accuracy via the usage of techniques like Stochastic Gradient Descent and also employ an algorithm to find the perfect depth of the convolutional part of the network for different datasets. A drop of 33% was observed in the number of iterations required to get the algorithm to its original proficiency as claimed by Oriol et al.
AB - Generating descriptions for visual data (images and video) automatically has been a complicated task in the field of Computer Vision and Artificial Intelligence. This paper discusses the working of and improvements on an algorithm called Neural Image Captioner (NIC) by Oriol Vinyals and his team, which uses a deep convolutional and recurrent architecture to generate natural language sentences to describe the visual data input. We look at the possibility of making this algorithm train faster without allowing it to lose accuracy via the usage of techniques like Stochastic Gradient Descent and also employ an algorithm to find the perfect depth of the convolutional part of the network for different datasets. A drop of 33% was observed in the number of iterations required to get the algorithm to its original proficiency as claimed by Oriol et al.
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UR - http://www.scopus.com/inward/citedby.url?scp=84979009562&partnerID=8YFLogxK
U2 - 10.1109/RAICS.2015.7488398
DO - 10.1109/RAICS.2015.7488398
M3 - Conference contribution
AN - SCOPUS:84979009562
SP - 110
EP - 115
BT - 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -