Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding

Hao, Jiachang; Sun, Haifeng; Ren, Pengfei; Wang, Jingyu; Qi, Qi; Liao, Jianxin

doi:10.1007/978-3-031-20059-5_8

Cited by 16 publications

(3 citation statements)

References 57 publications

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“…Charades-CD. In Table 4, we summarized the performance of several models on Charades-CD, including models designed to solve the temporal bias problem [39,48]. BM-DETR shows competitive performances compared to baseline models, and our results also suggest that utilizing temporally augmented videos can enhance the model's capability for generalization against temporal distributions.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 97%

“…Table 4: Performance results on Charades-CD. Since the authors [39] did not name the proposed framework, we call it "Shuffling" from the paper's title.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Recent studies [37][38][39][40] explored techniques for augmenting temporal moments in videos and demonstrated that these techniques work well for localization tasks. Inspired by this, we leverage a temporal shifting method that moves the ground-truth moment to a new (random) temporal position.…”

Section: Temporal Shiftingmentioning

confidence: 99%

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A Study on the Bedload Discharge Estimation using CNN

Jung¹,

Jun²,

Kim³

et al. 2023

Preprint

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Localized torrential rain, which has recently increased in frequency due to abnormal climate, accelerates erosion in the river basin and increases sediment transport into the river. The movement of inflowed sediment is one of the most important factors in the development and management of water resources. Among the mechanisms of sediment transport in rivers, bedload has limitations in direct measurement due to the risk it poses and inaccuracy in the existing measurement methods. Measurement equipment based on new concepts is continuously being developed to overcome these limitations. A representative equipment is a pipe hydrophone, which indirectly measures the bedload discharge by collecting and analyzing acoustic data when soil collides with a metal tube with a built-in microphone. To estimate the bedload discharge, this study acquired data through indoor experiment and applied them to the learning process of the Convolutional Neural Networks(CNN). First, an indoor hydraulic experiment device was built with a pipe hydrophone installed at the bottom of the water outlet of the indoor waterway. Then, a system for analyzing and displaying graphs for the impact sound of bedload, and data acquisition storage programs therein, was established. Finally, learning for bedload discharge estimation was conducted using CNN, and the accuracy of the estimation was reviewed. As a result, the F1-score for the accuracy of bedload discharge estimation was 61%, and the accuracy was higher when bedload discharge was 3kg and 10kg, compared to other weight ranges. Considering that the accuracy of 61% is an insufficient level to completely trust the estimated result, more efficient measurement would be possible by combining this method with the previously developed measurement methods in a complementary manner. In future studies, additional experimental data under various conditions will be secured and applied, to increase the accuracy of bedload discharge estimation. &#160; "This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(C20017370001)"

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Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 97%

“…Table 4: Performance results on Charades-CD. Since the authors [39] did not name the proposed framework, we call it "Shuffling" from the paper's title.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%