Action Recognition News Today

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 inve...

Veterans and community members gathered in Port St. Lucie to remember prisoners of war and service members missing in action.

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.

ENTERPRISE, Ala. (WDHN) — The City of Enterprise honors the service, valor, and sacrifice of service members who were prisoners of war and those who never returned home. “Though they are not here, their sacrifice is not forgotten,” Mayor William E. Cooper said. “Today and every day at city hall we fly the iconic black […]

The Douglas County veterans memorial will be refreshed and refurbished starting this summer thanks to a grant awarded in early July.

Despite great progress achieved by transformer in various vision tasks, it is still underexplored for skeleton-based action recognition with only a few attempts. Besides, these methods directly calculate the pair-wise global self-attention equally for all the joints in both the spatial and temporal dimensions, undervaluing the effect of discriminative local joints and the short-range temporal dynamics. In this work, we propose a novel Focal and Global Spatial-Temporal Transformer network (FG-STF...

Skeleton data is widely used in human action recognition for easy access, computational efficiency and environmental robustness. Recently, encoding skeleton sequences into color images becomes a popular preprocessing procedure to make use of the spatial modeling ability of convolutional neural network (CNN). Furthermore, inspired by relevant work in other fields, attention mechanism has been introduced to CNN based skeleton action recognition. In this paper, we propose a two-branch hierarchical ...

MIT MEng students Yo-whan “John” Kim, Emma Liu, and Clemente Ocejo tackled and guided application-focused research with the MIT-IBM Watson AI Lab.

This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition. It is not only able to aggregate node information like a vanilla graph convolutional operation but also gradient information. Without introducing any additional parameters, CDGC can replace vanilla graph convolution in any existing Graph Convolutional Networks (GCNs). In addition, an accelerated version of the CDGC is developed which greatly improves th...