6,279 research outputs found
Scene Graph Generation via Conditional Random Fields
Despite the great success object detection and segmentation models have
achieved in recognizing individual objects in images, performance on cognitive
tasks such as image caption, semantic image retrieval, and visual QA is far
from satisfactory. To achieve better performance on these cognitive tasks,
merely recognizing individual object instances is insufficient. Instead, the
interactions between object instances need to be captured in order to
facilitate reasoning and understanding of the visual scenes in an image. Scene
graph, a graph representation of images that captures object instances and
their relationships, offers a comprehensive understanding of an image. However,
existing techniques on scene graph generation fail to distinguish subjects and
objects in the visual scenes of images and thus do not perform well with
real-world datasets where exist ambiguous object instances. In this work, we
propose a novel scene graph generation model for predicting object instances
and its corresponding relationships in an image. Our model, SG-CRF, learns the
sequential order of subject and object in a relationship triplet, and the
semantic compatibility of object instance nodes and relationship nodes in a
scene graph efficiently. Experiments empirically show that SG-CRF outperforms
the state-of-the-art methods, on three different datasets, i.e., CLEVR, VRD,
and Visual Genome, raising the Recall@100 from 24.99% to 49.95%, from 41.92% to
50.47%, and from 54.69% to 54.77%, respectively
Diffusion Dynamics, Moments, and Distribution of First Passage Time on the Protein-Folding Energy Landscape, with Applications to Single Molecules
We study the dynamics of protein folding via statistical energy-landscape
theory. In particular, we concentrate on the local-connectivity case with the
folding progress described by the fraction of native conformations. We obtain
information for the first passage-time (FPT) distribution and its moments. The
results show a dynamic transition temperature below which the FPT distribution
develops a power-law tail, a signature of the intermittency phenomena of the
folding dynamics. We also discuss the possible application of the results to
single-molecule dynamics experiments
Geo-Social Group Queries with Minimum Acquaintance Constraint
The prosperity of location-based social networking services enables
geo-social group queries for group-based activity planning and marketing. This
paper proposes a new family of geo-social group queries with minimum
acquaintance constraint (GSGQs), which are more appealing than existing
geo-social group queries in terms of producing a cohesive group that guarantees
the worst-case acquaintance level. GSGQs, also specified with various spatial
constraints, are more complex than conventional spatial queries; particularly,
those with a strict NN spatial constraint are proved to be NP-hard. For
efficient processing of general GSGQ queries on large location-based social
networks, we devise two social-aware index structures, namely SaR-tree and
SaR*-tree. The latter features a novel clustering technique that considers both
spatial and social factors. Based on SaR-tree and SaR*-tree, efficient
algorithms are developed to process various GSGQs. Extensive experiments on
real-world Gowalla and Dianping datasets show that our proposed methods
substantially outperform the baseline algorithms based on R-tree.Comment: This is the preprint version that is accepted by the Very Large Data
Bases Journa
Maximizing Friend-Making Likelihood for Social Activity Organization
The social presence theory in social psychology suggests that
computer-mediated online interactions are inferior to face-to-face, in-person
interactions. In this paper, we consider the scenarios of organizing in person
friend-making social activities via online social networks (OSNs) and formulate
a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by
modeling both existing friendships and the likelihood of new friend making. To
find a set of attendees for socialization activities, HMGF is unique and
challenging due to the interplay of the group size, the constraint on existing
friendships and the objective function on the likelihood of friend making. We
prove that HMGF is NP-Hard, and no approximation algorithm exists unless P =
NP. We then propose an error-bounded approximation algorithm to efficiently
obtain the solutions very close to the optimal solutions. We conduct a user
study to validate our problem formulation and per- form extensive experiments
on real datasets to demonstrate the efficiency and effectiveness of our
proposed algorithm
A Chinese version of the psychotic symptom rating scales : psychometric properties in recent-onset and chronic psychosis
2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe
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