Intention-Conditioned Long-Term Human Egocentric Action Forecasting
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| Format: | Preprint |
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2022
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| _version_ | 1866913302945202176 |
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| author | Mascaro, Esteve Valls Ahn, Hyemin Lee, Dongheui |
| author_facet | Mascaro, Esteve Valls Ahn, Hyemin Lee, Dongheui |
| contents | To anticipate how a human would act in the future, it is essential to understand the human intention since it guides the human towards a certain goal. In this paper, we propose a hierarchical architecture which assumes a sequence of human action (low-level) can be driven from the human intention (high-level). Based on this, we deal with Long-Term Action Anticipation task in egocentric videos. Our framework first extracts two level of human information over the N observed videos human actions through a Hierarchical Multi-task MLP Mixer (H3M). Then, we condition the uncertainty of the future through an Intention-Conditioned Variational Auto-Encoder (I-CVAE) that generates K stable predictions of the next Z=20 actions that the observed human might perform. By leveraging human intention as high-level information, we claim that our model is able to anticipate more time-consistent actions in the long-term, thus improving the results over baseline methods in EGO4D Challenge. This work ranked first in both CVPR@2022 and ECVV@2022 EGO4D LTA Challenge by providing more plausible anticipated sequences, improving the anticipation of nouns and overall actions. Webpage: https://evm7.github.io/icvae-page/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2207_12080 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Intention-Conditioned Long-Term Human Egocentric Action Forecasting Mascaro, Esteve Valls Ahn, Hyemin Lee, Dongheui Computer Vision and Pattern Recognition To anticipate how a human would act in the future, it is essential to understand the human intention since it guides the human towards a certain goal. In this paper, we propose a hierarchical architecture which assumes a sequence of human action (low-level) can be driven from the human intention (high-level). Based on this, we deal with Long-Term Action Anticipation task in egocentric videos. Our framework first extracts two level of human information over the N observed videos human actions through a Hierarchical Multi-task MLP Mixer (H3M). Then, we condition the uncertainty of the future through an Intention-Conditioned Variational Auto-Encoder (I-CVAE) that generates K stable predictions of the next Z=20 actions that the observed human might perform. By leveraging human intention as high-level information, we claim that our model is able to anticipate more time-consistent actions in the long-term, thus improving the results over baseline methods in EGO4D Challenge. This work ranked first in both CVPR@2022 and ECVV@2022 EGO4D LTA Challenge by providing more plausible anticipated sequences, improving the anticipation of nouns and overall actions. Webpage: https://evm7.github.io/icvae-page/ |
| title | Intention-Conditioned Long-Term Human Egocentric Action Forecasting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2207.12080 |