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Language-guided smart systems can help to design next-generation human-machine interactive applications. The dense text description is one of the research areas where systems learn the semantic knowledge and visual features of each video frame and map them to describe the video's most relevant subjects and events. In this paper, we consider untrimmed sports videos as our case study. Generating dense descriptions in the sports domain to supplement journalistic works without relying on commentators and experts requires more investigation. Motivated by this, we propose an end-to-end automated text-generator, SpecTextor, that learns the semantic features from untrimmed videos of sports games and generates associated descriptive texts. The proposed approach considers the video as a sequence of frames and sequentially generates words. After splitting videos into frames, we use a pre-trained VGG-16 model for feature extraction and encoding the video frames. With these encoded frames, we posit a Long Short-Term Memory (LSTM) based attention-decoder pipeline that leverages soft-attention mechanism to map the semantic features with relevant textual descriptions to generate the explanation of the game. Because developing a comprehensive description of the game warrants training on a set of dense time-stamped captions, we leverage two available public datasets: ActivityNet Captions and Microsoft Video Description. In addition, we utilized two different decoding algorithms: beam search and greedy search and computed two evaluation metrics: BLEU and METEOR scores.
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Predicting Video Affect via Induced Affection in the Wild
Curating large and high quality datasets for studying affect is a costly and time consuming process, especially when the labels are continuous. In this paper, we examine the potential to use unlabeled public reactions in the form of textual comments to aid in classifying video affect. We examine two popular datasets used for affect recognition and mine public reactions for these videos. We learn a representation of these reactions by using the video ratings as a weakly supervised signal. We show that our model can learn a fine-grained prediction of comment affect when given a video alone. Furthermore, we demonstrate how predicting the affective properties of a comment can be a potentially useful modality to use in multimodal affect modeling.
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- Award ID(s):
- 1845587
- PAR ID:
- 10332229
- Date Published:
- Journal Name:
- ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction
- Page Range / eLocation ID:
- 442 to 451
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3382507.3418838
