In this paper, we propose a novel method for cross-view action recognition via a continuous
virtual path which connects the source view and the target view. Each point on this virtual …

This work focuses on the problem of multi-label learning with missing labels (MLML), which
aims to label each test instance with multiple class labels given training instances that have …

This paper proposes a unified framework for multiple/single-view human action recognition.
First, we propose the hierarchical partwise bag-of-words representation which encodes both …

Shift graph convolutional network (Shift-GCN) achieves remarkable performance for
skeleton based action recognition with lower computational complexity than other GCN …

In this paper, we propose a novel deep model named integration convolutional neural
network (ICNN) for person re-identification in camera networks, which jointly learns global …

High-level semantic feature is important to recognize human action. Recently, relative
attributes, which are used to describe relative relationship, have been proposed as one of …

Recently, attributes have been introduced as a kind of high-level semantic information to
help improve the classification accuracy. Multitask learning is an effective methodology to …

The structure of data is important to the recognition of data. It is a fundamental question how
measures and complements the structure of multi-features, because the fusion structure of …

In this paper, we present a novel two-layer video representation for human action
recognition employing hierarchical group sparse encoding technique and spatio-temporal …

Recently, maximum margin clustering (MMC) has been proposed for a cross-view action
recognition. However, such a method neglects the temporal relationship between …