|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM367901749 |
| 003 |
DE-627 |
| 005 |
20240207232309.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
240202s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2024.3359045
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1283.xml
|
| 035 |
|
|
|a (DE-627)NLM367901749
|
| 035 |
|
|
|a (NLM)38300778
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Song, Peipei
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Emotional Video Captioning With Vision-Based Emotion Interpretation Network
|
| 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 Completed 07.02.2024
|
| 500 |
|
|
|a Date Revised 07.02.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Effectively summarizing and re-expressing video content by natural languages in a more human-like fashion is one of the key topics in the field of multimedia content understanding. Despite good progress made in recent years, existing efforts usually overlooked the emotions in user-generated videos, thus making the generated sentence a bit boring and soulless. To fill the research gap, this paper presents a novel emotional video captioning framework in which we design a Vision-based Emotion Interpretation Network to effectively capture the emotions conveyed in videos and describe the visual content in both factual and emotional languages. Specifically, we first model the emotion distribution over an open psychological vocabulary to predict the emotional state of videos. Then, guided by the discovered emotional state, we incorporate visual context, textual context, and visual-textual relevance into an aggregated multimodal contextual vector to enhance video captioning. Furthermore, we optimize the network in a new emotion-fact coordinated way that involves two losses- Emotional Indication Loss and Factual Contrastive Loss, which penalize the error of emotion prediction and visual-textual factual relevance, respectively. In other words, we innovatively introduce emotional representation learning into an end-to-end video captioning network. Extensive experiments on public benchmark datasets, EmVidCap and EmVidCap-S, demonstrate that our method can significantly outperform the state-of-the-art methods by a large margin. Quantitative ablation studies and qualitative analyses clearly show that our method is able to effectively capture the emotions in videos and thus generate emotional language sentences to interpret the video content
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Guo, Dan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yang, Xun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tang, Shengeng
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Meng
|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 33(2024) vom: 01., Seite 1122-1135
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:33
|g year:2024
|g day:01
|g pages:1122-1135
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2024.3359045
|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 33
|j 2024
|b 01
|h 1122-1135
|