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The novel subject triplet loss provides the best performance overall, and all personalized deep embeddings out-perform our baseline personalized engineered feature embedding and an impersonal fully convolutional neural network classifier.
Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).
Spatio-temporal action localization is a challenging yet fascinating task that aims to detect and classify human actions in video clips.
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.
Diffusions effectively interact two aspects of information, i. e., localized and holistic, for more powerful way of representation learning.
In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector---a progressive learning framework for spatio-temporal action detection in videos.
Recognizing human actions is a core challenge for autonomous systems as they directly share the same space with humans.
#3 best model for Skeleton Based Action Recognition on JHMDB (2D poses only)
Each branch produces a set of action anchor layers by applying deconvolution to the feature maps of the main stream.
With only half the computation and parameters of the state-of-the-art two-stream methods, our two-in-one stream still achieves impressive results on UCF101-24, UCFSports and J-HMDB.