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Uncertainty-Based Spatial-Temporal Attention for Online Action Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Online action detection aims at detecting the ongoing action in a streaming video. In this paper, we proposed an uncertainty-based spatial-temporal attention for online action detection. By explicitly modeling the distribution of model parameters, we extend the baseline models in a probabilistic manner. Then we quantify the predictive uncertainty and use it to generate spatial-temporal attention that focus on large mutual information regions and frames. For inference, we introduce a two-stream framework that combines the baseline model and the probabilistic model based on the input uncertainty. We validate the effectiveness of our method on three benchmark datasets: THUMOS-14, TVSeries, and HDD. Furthermore, we demonstrate that our method generalizes better under different views and occlusions, and is more robust when training with small-scale data.

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Acknowledgement

This project is supported in part by a gift from Wormpex AI Research to Rensselaer Polytechnic Institute.

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Correspondence to Qiang Ji .

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Guo, H., Ren, Z., Wu, Y., Hua, G., Ji, Q. (2022). Uncertainty-Based Spatial-Temporal Attention for Online Action Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_5

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