Graph convolutional networks (GCNs) are widely used in skeleton-based action recognition. It is known that the graph topology is a vital part in GCNs, and different kinds of graph topologies have been proposed for skeleton-based action recognition, mostly based on a predefined topology and a dynamically learned one. The predefined topology is based on the human intuition for skeleton (the connectivity of joints) and has not been investigated whether it is optimal. In this paper, we focus on investigating this static graph topology and propose to generate a learned static graph topology for skeleton. To be specific, a temporal frame-wise and channel-wise topology-based GCNs (TC-GCNs) are developed, where, instead of using a predefined topology by human, a topology is learned for skeleton-based action recognition. The TC-GCNs consist of generating a temporal frame-wise topology and a channel-wise topology to formulate the relationship of skeleton joints in the temporal dimension and chan
China Strives for Super SIMs – Identity News Digest findbiometrics.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from findbiometrics.com Daily Mail and Mail on Sunday newspapers.
Between World War II, the Korean War, the Vietnam War, the Cold War, the Gulf War and other recent conflicts more than 80,000 Americans remain missing.
On Friday dozens of veterans, military officials and the families of missing military personnel gathered at the National Memorial Cemetery of the Pacific at Punchbowl to commemorate National Prisoner of War and Missing in Action Recognition Day.