scholarly journals Fast coarse-to-fine video retrieval using shot-level spatio-temporal statistics

2006 ◽  
Vol 16 (5) ◽  
pp. 642-648 ◽  
Author(s):  
Yu-Hsuan Ho ◽  
C.-W. Lin ◽  
Jing-Fung Chen ◽  
H.-Y.M. Liao
Keyword(s):  
Video Retrieval ◽  
Temporal Statistics ◽  
Spatio Temporal ◽  
Coarse To Fine

State-of-the-art on spatio-temporal information-based video retrieval

2009 ◽  
Vol 42 (2) ◽  
pp. 267-282 ◽  
Author(s):  
W. Ren ◽  
S. Singh ◽  
M. Singh ◽  
Y.S. Zhu
Keyword(s):  
Video Retrieval ◽  
State Of The Art ◽  
Temporal Information ◽  
Spatio Temporal

scholarly journals Multi-Instance Multi-Label Action Recognition and Localization Based on Spatio-Temporal Pre-Trimming for Untrimmed Videos

2020 ◽  
Vol 34 (07) ◽  
pp. 12886-12893
Author(s):  
Xiao-Yu Zhang ◽  
Haichao Shi ◽  
Changsheng Li ◽  
Peng Li
Keyword(s):  
Action Recognition ◽  
Human Action ◽  
Learning Problem ◽  
The Arts ◽  
Key Factor ◽  
Benchmark Datasets ◽  
Spatio Temporal ◽  
Weakly Supervised ◽  
Temporal Localization ◽  
Coarse To Fine

Weakly supervised action recognition and localization for untrimmed videos is a challenging problem with extensive applications. The overwhelming irrelevant background contents in untrimmed videos severely hamper effective identification of actions of interest. In this paper, we propose a novel multi-instance multi-label modeling network based on spatio-temporal pre-trimming to recognize actions and locate corresponding frames in untrimmed videos. Motivated by the fact that person is the key factor in a human action, we spatially and temporally segment each untrimmed video into person-centric clips with pose estimation and tracking techniques. Given the bag-of-instances structure associated with video-level labels, action recognition is naturally formulated as a multi-instance multi-label learning problem. The network is optimized iteratively with selective coarse-to-fine pre-trimming based on instance-label activation. After convergence, temporal localization is further achieved with local-global temporal class activation map. Extensive experiments are conducted on two benchmark datasets, i.e. THUMOS14 and ActivityNet1.3, and experimental results clearly corroborate the efficacy of our method when compared with the state-of-the-arts.

scholarly journals Fast coarse-to-fine video retrieval via shot-level statistics

2005 ◽  
Author(s):  
Yu-Hsuan Ho ◽  
Chia-Wen Lin ◽  
Jing-Fung Chen ◽  
Hong-Yuan M. Liao
Keyword(s):  
Video Retrieval ◽  
Level Statistics ◽  
Coarse To Fine
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