Kinetics and Scene Features for Intent Detection
| Autor: | Ching-Yung Lin, Serena Yuan, Tianle Zhu, Ziyin Wang, Vishal Anand, Wenfeng Lyu, Raksha Ramesh |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Research areas business.industry Computer science Cognitive neuroscience of visual object recognition computer.software_genre Activity recognition Information extraction Relational knowledge Video tracking Artificial intelligence Baseline (configuration management) business computer Natural language processing |
| Zdroj: | ICMI Companion |
| DOI: | 10.1145/3395035.3425641 |
| Popis: | We create multi-modal fusion models to predict relational classes within entities in free-form inputs such as unseen movies. Our approach identifies information rich features within individual sources -- emotion, text-attention, age, gender, and contextual background object tracking. These information are absorbed and contrasted from baseline fusion architectures. These five models then showcase future research areas on this challenging problem of relational knowledge extraction from movies and free-form multi-modal input sources. We find that, generally, the Kinetics model added with Attributes and Objects beat the baseline models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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