As a crucial step for analyzing human actions, hand-object interaction understanding is researchworthy in a broad range of applications related to virtual or augmented reality. Current works largely focus on hand-object pose estimation (HOPE) [16, 19, 21], which aims to capture the pose configuration of the given hand-object image.

"equiconnected", which is a stronger result that we will define and introduce later in the paper.

We present an application of the connectedness of these superlevel sets to the derivation of minimax theorems for robust reinforcement learning.

Although theteacher haslearned rich and powerful representations, it also contains unignorable bias knowledge which is usually induced by the context prior (e.g., background) in the training data.