Subjective video quality evaluation for user generated contents via textual prompts
The explosion of user-generated content (UGC) videos has transformed the online landscape. Platforms like TikTok and YouTube overflow with UGC, offering a diverse range of content consumed by billions of viewers. While this presents exciting avenues for creative expression and content consumption, i...
Saved in:
主要作者: | Ang, Dario |
---|---|
其他作者: | Lin Weisi |
格式: | Final Year Project |
語言: | English |
出版: |
Nanyang Technological University
2024
|
主題: | |
在線閱讀: | https://hdl.handle.net/10356/176019 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
相似書籍
-
Towards textually describing complex video contents with audio-visual concept classifiers
由: TAN, Chun Chet, et al.
出版: (2011) -
ClusterPrompt: Cluster semantic enhanced prompt learning for new intent discovery
由: LIANG, Jinggui, et al.
出版: (2023) -
Reviewing multimodal deep learning techniques for user-generated content analysis
由: Sachin, Surawar Sanath
出版: (2023) -
Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration
由: DENG, Yang, et al.
出版: (2023) -
Just adjust one prompt: Enhancing in-context dialogue scoring via constructing the optimal subgraph of demonstrations and prompts
由: PU, Jiashu, et al.
出版: (2023)