Temporal video scene segmentation using deep-learning

Trojahn, Tiago Henrique; Goularte, Rudinei

doi:10.1007/s11042-020-10450-2

Cited by 9 publications

(3 citation statements)

References 57 publications

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“…Pei et al considered scene boundary detection a graph-linking problem in which a graph convolutional network (GCN) was trained to link shots based on the pivot graph generated using cosine similarity among shots [ 18 ]. Trojahn et al used a video key-frames selector (VKFrameS

[ 39 ] to determine the key-frames within the shots based on visual, audio, and textual features [ 12 ]. Selected key-frames were analyzed as a sequence going through an LSTM network for detecting scene boundaries.…”

Section: Related Workmentioning

confidence: 99%

“…TELNet was trained and evaluated on multiple publicly available datasets and compared to other state-of-the-art models [ 9 , 11 , 12 , 13 , 14 , 15 ]. TELNet achieved results comparable to the other SOTA models without knowing the number of video scenes in the canonical settings and improved on their results (F-Score) significantly in the transfer setting (cross-dataset evaluations).…”

Section: Introductionmentioning

confidence: 99%

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Video Scene Detection Using Transformer Encoding Linker Network (TELNet)

Tseng

Yeh

et al. 2023

Sensors

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This paper introduces a transformer encoding linker network (TELNet) for automatically identifying scene boundaries in videos without prior knowledge of their structure. Videos consist of sequences of semantically related shots or chapters, and recognizing scene boundaries is crucial for various video processing tasks, including video summarization. TELNet utilizes a rolling window to scan through video shots, encoding their features extracted from a fine-tuned 3D CNN model (transformer encoder). By establishing links between video shots based on these encoded features (linker), TELNet efficiently identifies scene boundaries where consecutive shots lack links. TELNet was trained on multiple video scene detection datasets and demonstrated results comparable to other state-of-the-art models in standard settings. Notably, in cross-dataset evaluations, TELNet demonstrated significantly improved results (F-score). Furthermore, TELNet’s computational complexity grows linearly with the number of shots, making it highly efficient in processing long videos.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Video Scene Detection Using Transformer Encoding Linker Network (TELNet)

Tseng

Yeh

et al. 2023

Sensors

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“…We can see that each multimodal teaching approach has several pedagogies to choose from; in other words, the teaching methods in multimodal teaching approaches are flexible and varied. The teaching method is only a means to accomplish the classroom goals, and there should not be only one teaching method consistently throughout the teaching process but should be refined to different teaching sessions to choose the appropriate teaching method according to the specific teaching situation [21]. The quality of model input data should be improved, then the basic model used in this article should be introduced, which is a pure speech model based on time series structure.…”

Section: Analysis Of Automatic Assessment Of Spokenmentioning

confidence: 99%

An Automatic Assessment Method for Spoken English Based on Multimodal Feature Fusion

Zhang

2021

Wireless Communications and Mobile Computing

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This paper provides a comprehensive explanation of the theoretical foundations of multimodal discourse analysis theory as applied to speaking instructional design. The specific application of multimodal theory in the teaching of elementary English speaking classrooms is explored through the teaching design of elementary English speaking classrooms, the teaching implementation of multimodal teaching design is carried out, and the effect of the teaching practice of elementary English speaking guided by multimodal discourse analysis theory is comprehensively evaluated through classroom observation method, questionnaire survey method, and interview method, combined with the teaching evaluation and teaching implementation effect, which is the multimodal teaching design. The paper also summarizes the findings and shortcomings of the study. Through the teaching design and implementation, the advantages of multimodal teaching are obvious; it can combine with modern advanced teaching techniques to create more realistic communicative situations in the classroom, gather and present various modal resources and information, and ensure rich and diverse language input; students can receive various sensory stimuli in the classroom, deepen their memory and experience of language, increase the interest of classroom teaching, and improve students’ participation. It also increases the interest of the classroom and enhances students’ participation and motivation. Based on multimodal theory, the author designed a multimodal teaching framework for a semester-long speaking course in the speaking classroom for reference. The fuzzy measures were constructed based on subsets of language segments containing 10 phonemes belonging to the same HDP set. Finally, linguistic scores are given by the Surgeon integral model based on the plausibility of the system and the fuzzy measures. The experimental results based on Sphinx-4 show that the evaluation model yields plausible and stable evaluation results for the 3 test sets at an average correct recognition rate of 84.7% of phonemes.

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