|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM376504870 |
| 003 |
DE-627 |
| 005 |
20240823232755.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
240821s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2024.3431915
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1510.xml
|
| 035 |
|
|
|a (DE-627)NLM376504870
|
| 035 |
|
|
|a (NLM)39163178
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Luo, Wang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Adaptive Prototype Learning for Weakly-supervised Temporal Action Localization
|
| 264 |
|
1 |
|c 2024
|
| 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 23.08.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status Publisher
|
| 520 |
|
|
|a Weakly-supervised Temporal Action Localization (WTAL) aims to localize action instances with only video-level labels during training, where two primary issues are localization incompleteness and background interference. To relieve these two issues, recent methods adopt an attention mechanism to activate action instances and simultaneously suppress background ones, which have achieved remarkable progress. Nevertheless, we argue that these two issues have not been well resolved yet. On the one hand, the attention mechanism adopts fixed weights for different videos, which are incapable of handling the diversity of different videos, thus deficient in addressing the problem of localization incompleteness. On the other hand, previous methods only focus on learning the foreground attention and the attention weights usually suffer from ambiguity, resulting in difficulty of suppressing background interference. To deal with the above issues, in this paper we propose an Adaptive Prototype Learning (APL) method for WTAL, which includes two key designs: (1) an Adaptive Transformer Network (ATN) to explicitly model background and learn video-adaptive prototypes for each specific video, (2) an OT-based Collaborative (OTC) training strategy to guide the learning of prototypes and remove the ambiguity of the foreground-background separation by introducing an Optimal Transport (OT) algorithm into the collaborative training scheme between RGB and FLOW streams. These two key designs can work together to learn video-adaptive prototypes and solve the above two issues, achieving robust localization. Extensive experimental results on two standard benchmarks (THUMOS14 and ActivityNet) demonstrate that our proposed APL performs favorably against state-of-the-art methods
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Ren, Huan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhangd, Tianzhu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yang, Wenfei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Yongdong
|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 PP(2024) vom: 20. Aug.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:PP
|g year:2024
|g day:20
|g month:08
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2024.3431915
|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 PP
|j 2024
|b 20
|c 08
|