|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM325352755 |
| 003 |
DE-627 |
| 005 |
20231225192246.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2021 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2021.3078324
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1084.xml
|
| 035 |
|
|
|a (DE-627)NLM325352755
|
| 035 |
|
|
|a (NLM)33983884
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Huang, Linjiang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Modeling Sub-Actions for Weakly Supervised Temporal Action Localization
|
| 264 |
|
1 |
|c 2021
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 25.05.2021
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a As a challenging task of high-level video understanding, weakly supervised temporal action localization has attracted more attention recently. Due to the usage of video-level category labels, this task is usually formulated as the task of classification, which always suffers from the contradiction between classification and detection. In this paper, we describe a novel approach to alleviate the contradiction for detecting more complete action instances by explicitly modeling sub-actions. Our method makes use of three innovations to model the latent sub-actions. First, our framework uses prototypes to represent sub-actions, which can be automatically learned in an end-to-end way. Second, we regard the relations among sub-actions as a graph, and construct the correspondences between sub-actions and actions by the graph pooling operation. Doing so not only makes the sub-actions inter-dependent to facilitate the multi-label setting, but also naturally use the video-level labels as weak supervision. Third, we devise three complementary loss functions, namely, representation loss, balance loss and relation loss to ensure the learned sub-actions are diverse and have clear semantic meanings. Experimental results on THUMOS14 and ActivityNet1.3 datasets demonstrate the effectiveness of our method and superior performance over state-of-the-art approaches
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Huang, Yan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Ouyang, Wanli
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Liang
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 19., Seite 5154-5167
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:30
|g year:2021
|g day:19
|g pages:5154-5167
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2021.3078324
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 30
|j 2021
|b 19
|h 5154-5167
|