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عنوان فارسی:
سیستم‌های همجوشی چندوجهی مبتنی بر نقاط مشاهده برای تشخیص عمل اسکلت
عنوان انگلیسی:
Observation Points Based Multi-modal Fusion Systems for Skeleton Action Recognition
Abstract
Current methods for skeleton-based action recognition compute features based on the given skeleton joint information. We show that introducing new observation points in skeleton motion sequences and using them to create fused representations from multiple modalities such as joints and bones, can enhance the discriminative power of the original modalities. Moreover, such representations can be used to create new streams in multi-stream networks that fuse constructively with other streams trained on the original modalities, effectively exhibiting a dual behaviour and collectively boosting the performance of the network even further. In this work, we present certain configurations of multi-modal fusion systems with observation points that can easily be incorporated in existing networks and improve state-of-the-art results on the two popular J-HMDB and Kinetics-Skeleton action recognition datasets.
عنوان فارسی:
جداسازی کور برای منابع متناوب از طریق یادگیری دیکشنری پراکنده
عنوان انگلیسی:
Blind Separation for Intermittent Sources via Sparse Dictionary Learning
Abstract
Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on the output produced by the former. Both stages are designed so as to account for the sparsity and memory of the sources. To this end, smooth LASSO is integrated with DL, while the forward-backward algorithm and Expectation Maximization (EM) algorithm are leveraged for PSF. It is shown that the proposed algorithm can enhance the detection and the estimation performance of the sources, and that it is robust to the sparsity level and average duration of transmission of the source signals.