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Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content.
#2 best model for Temporal Action Localization on ActivityNet-1.3
This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed.
In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector---a progressive learning framework for spatio-temporal action detection in videos.
In this paper, we introduce the concept of learning latent super-events from activity videos, and present how it benefits activity detection in continuous videos.
#2 best model for Action Detection on Charades (using extra training data)
While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context.
Diffusions effectively interact two aspects of information, i. e., localized and holistic, for more powerful way of representation learning.