This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.
Using a similar approach to collect data as the MOSI and EmoReact corpora, the GIFGIF+ dataset @cite_18 has video clip segments of emotional expressions. The difference between this dataset and others is that it provides only vision information of the expressions. Annotation was performed using an online self-developed platform in a crowd-sourced manner and currently, the dataset boasts more than 3 million annotations. The data is available in GIF format and not as videos which, according to the authors, provides a challenge on different frame rates per sequence. This dataset presents an impressive number of annotations per video clip, which contributes to the universal opinion on the annotation categories. However, the dataset presents unrealistic frame rates within their video clips, with the highest frame rate being 40 times more than the slowest one. Also evident is that, for only providing one modality, this dataset is restricted to vision-only applications.
@cite_18
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Animated GIFs are widely used on the Internet to express emotions, but their automatic analysis is largely unexplored. Existing GIF datasets with emotion labels are too small for training contemporary machine learning models, so we propose a semi-automatic method to collect emotional animated GIFs from the Internet with the least amount of human labor. The method trains weak emotion recognizers on labeled data, and uses them to sort a large quantity of unlabeled GIFs. We found that by exploiting the clustered structure of emotions, the number of GIFs a labeler needs to check can be greatly reduced. Using the proposed method, a dataset called GIFGIF+ with 23,544 GIFs over 17 emotions was created, which provides a promising platform for affective computing research.