Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the citation relationships in scholarly publications where a paper is cited by others not because they have the same content, but because they share one or multiple subject matters. To date, while many network embedding methods exist to take the node content into consideration, they all consider node content as simple flat word/attribute set and nodes sharing connections are assumed to have dependency with respect to all words or attributes. In this paper, we argue that considering topic-level semantic interactions between nodes is crucial to learn discriminative node embedding vectors. In order to model pairwise topic relevance between linked text nodes, we propose topical network embedding, where interactions between nodes are built on the shared latent topics. Accordingly, we propose a unified optimization framework to simultaneously learn topic and node representations from the network text contents and structures, respectively. Meanwhile, the structure modeling takes the learned topic representations as conditional context under the principle that two nodes can infer each other contingent on the shared latent topics.more »
A Stochastic Attribute Grammar for Robust Cross-View Human Tracking
In computer vision, tracking humans across camera views
remains challenging, especially for complex scenarios with frequent
occlusions, significant lighting changes and other difficulties. Under
such conditions, most existing appearance and geometric cues are
not reliable enough to distinguish humans across camera views. To
address these challenges, this paper presents a stochastic attribute
grammar model for leveraging complementary and discriminative human
attributes for enhancing cross-view tracking. The key idea of our
method is to introduce a hierarchical representation, parse graph, to
describe a subject and its movement trajectory in both space and time
domains. This results in a hierarchical compositional representation,
comprising trajectory entities of varying level, including human boxes,
3D human boxes, tracklets and trajectories. We use a set of grammar
rules to decompose a graph node (e.g. tracklet) into a set of children
nodes (e.g. 3D human boxes), and augment each node with a set
of attributes, including geometry (e.g., moving speed, direction), accessories
(e.g., bags), and/or activities (e.g., walking, running). These
attributes serve as valuable cues, in addition to appearance features
(e.g., colors), in determining the associations of human detection boxes
across cameras. In particular, the attributes of a parent node are inherited
by its children nodes, resulting in consistency constraints over
the feasible parse graph. Thus, we cast cross-view human tracking as
finding the most discriminative parse graph for more »
- Award ID(s):
- 1657600
- Publication Date:
- NSF-PAR ID:
- 10056964
- Journal Name:
- IEEE transactions on circuits and systems for video technology
- ISSN:
- 1558-2205
- Sponsoring Org:
- National Science Foundation
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