第一步当然是找一个合适的数据集,目前用的最多的MSA数据集自然是CMU-MOSEI。 CMU-MOSEI介绍 CMU-MOSEI数据集是目前最大的MSA数据集之一,具有sentiment和emotion两个标签,sentiment标签的数值在[−3,3]之间,主要用来衡量是情绪的positive和negative的程度。emotion标签包含愤怒、开心、悲伤、惊讶、害怕和厌恶6个类别,...
再使用CMU-Multimodal SDK Version 1.2.0提取CMU- Mosei数据集的特征时,程序运行到一半叫我“Please input dimension namescomputational sequence version for computational sequence: ” 该输入什么?本帖最后由 于2024-11-15 17:43:49 编辑 雨落无痕 帖子 6 回复 639 您好,从描述上看,似乎是需要输入相关的数据...
CMU-MOSEI: CMU-MOSEI contains a large number of in-the-wild videos annotated for sentiment and emotions. The annotations follow consensus-based online perceptions. Alongside the value of this dataset for sentiment and emotion recognition, it is also suitable for representation learning due to large ...
but I do advocate exploring the datasets using the SDK. For example try different alignments, or strategies. (Please note that CMU-MOSEI had some issues for some videos over their acoustic modality. They are now solved and CMU-MOSEI downloaded from SDK gets better performance than the one we...
Still we highly recommend using SDK since you will have access to the latest updates for the datasets. --> Alignment function on large datasets improved ~40x in speed. CMU-MOSEI now aligns in less than 4 hours. Previously the full dataset took around 2-3 days to fully align, majority ...
CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) is the largest dataset of sentence-level sentiment analysis and emotion recognition in online videos. CMU-MOSEI contains over 12 hours of annotated video from over 1000 speakers and 250 topics.Bench...
CMU-MOSEI: CMU-MOSEI contains a large number of in-the-wild videos annotated for sentiment and emotions. The annotations follow consensus-based online perceptions. Alongside the value of this dataset for sentiment and emotion recognition, it is also suitable for representation learning due to...
MultiModal Sentiment Analysis architectures for CMU-MOSEI. Description The repository contains four multimodal architectures and relative training and test functions for sentiment analysis on CMU-MOSEI. Inside the data folder, transcriptions and labels are provided for the standard training, validation and ...
but I do advocate exploring the datasets using the SDK. For example try different alignments, or strategies. (Please note that CMU-MOSEI had some issues for some videos over their acoustic modality. They are now solved and CMU-MOSEI downloaded from SDK gets better performance than the one we...
Still we highly recommend using SDK since you will have access to the latest updates for the datasets.--> Alignment function on large datasets improved ~40x in speed. CMU-MOSEI now aligns in less than 4 hours. Previously the full dataset took around 2-3 days to fully align, majority of...