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To address these difficulties, we introduce the Boundary-Matching (BM) mechanism to evaluate confidence scores of densely distributed proposals, which denote a proposal as a matching pair of starting and ending boundaries and combine all densely distributed BM pairs into the BM confidence map.
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
An event happening in the world is often made of different activities and actions that can unfold simultaneously or sequentially within a few seconds.
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)