Abstract
In this paper, we report our efforts and challenges on the TRECVID ad-hoc video search (AVS) task. The goal of the AVS task it to build a zero-shot video retrieval system using a complicated query phrase. Our system has the following two characteristics. First, we prepared a large number of pre-trained concept classifiers in advance that can detect various kinds of objects, persons, scenes, and actions. This strategy contributes to improve the word coverage rate of keywords in query phrases. Second, we selected additional concept classifiers by natural language processing techniques such as using word similarities or synonyms. We submitted our systems with these two characteristics to the TRECVID AVS task in 2016 and 2017, and one of our systems ranked the highest among all the submitted systems for the second consecutive year.
| Original language | English |
|---|---|
| Pages (from-to) | 983-990 |
| Number of pages | 8 |
| Journal | Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering |
| Volume | 84 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
| Externally published | Yes |
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Keywords
- Ad-hoc video search
- Convolutional neural network
- TRECVID
- Video retrieval
- Zero-shot learning
ASJC Scopus subject areas
- Mechanical Engineering
Cite this
Zero-shot video retrieval from a query phrase including multiple concepts - Efforts and challenges in trecvid avs task-. / Ueki, Kazuya; Hirakawa, Koji; Kikuchi, Kotaro; Kobayashi, Tetsunori.
In: Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering, Vol. 84, No. 12, 01.01.2018, p. 983-990.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Zero-shot video retrieval from a query phrase including multiple concepts - Efforts and challenges in trecvid avs task-
AU - Ueki, Kazuya
AU - Hirakawa, Koji
AU - Kikuchi, Kotaro
AU - Kobayashi, Tetsunori
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In this paper, we report our efforts and challenges on the TRECVID ad-hoc video search (AVS) task. The goal of the AVS task it to build a zero-shot video retrieval system using a complicated query phrase. Our system has the following two characteristics. First, we prepared a large number of pre-trained concept classifiers in advance that can detect various kinds of objects, persons, scenes, and actions. This strategy contributes to improve the word coverage rate of keywords in query phrases. Second, we selected additional concept classifiers by natural language processing techniques such as using word similarities or synonyms. We submitted our systems with these two characteristics to the TRECVID AVS task in 2016 and 2017, and one of our systems ranked the highest among all the submitted systems for the second consecutive year.
AB - In this paper, we report our efforts and challenges on the TRECVID ad-hoc video search (AVS) task. The goal of the AVS task it to build a zero-shot video retrieval system using a complicated query phrase. Our system has the following two characteristics. First, we prepared a large number of pre-trained concept classifiers in advance that can detect various kinds of objects, persons, scenes, and actions. This strategy contributes to improve the word coverage rate of keywords in query phrases. Second, we selected additional concept classifiers by natural language processing techniques such as using word similarities or synonyms. We submitted our systems with these two characteristics to the TRECVID AVS task in 2016 and 2017, and one of our systems ranked the highest among all the submitted systems for the second consecutive year.
KW - Ad-hoc video search
KW - Convolutional neural network
KW - TRECVID
KW - Video retrieval
KW - Zero-shot learning
UR - http://www.scopus.com/inward/record.url?scp=85057768067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057768067&partnerID=8YFLogxK
U2 - 10.2493/jjspe.84.983
DO - 10.2493/jjspe.84.983
M3 - Article
AN - SCOPUS:85057768067
VL - 84
SP - 983
EP - 990
JO - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
JF - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
SN - 0912-0289
IS - 12
ER -