کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6937428 1449735 2018 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
DAAL: Deep activation-based attribute learning for action recognition in depth videos
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
DAAL: Deep activation-based attribute learning for action recognition in depth videos
چکیده انگلیسی
In this paper, we propose a joint semantic preserving action attribute learning framework for action recognition from depth videos, which is built on multi-stream deep neural networks. More specifically, this paper describes the idea to explore action attributes learned from deep activations. Multiple stream deep neural networks rather than conventional hand-crafted low-level features are employed to learn the deep activations. An undirected graph is utilized to model the complex semantics among action attributes and is integrated into our proposed joint action attribute learning algorithm. Experiments on several public datasets for action recognition demonstrate that 1) the deep activations achieve the state-of-the-art discriminative performance as feature vectors and 2) the attribute learner can produce generic attributes, and thus obtains decent performance on zero-shot action recognition.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 167, February 2018, Pages 37-49
نویسندگان
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