964 research outputs found
Convolutional Feature Masking for Joint Object and Stuff Segmentation
The topic of semantic segmentation has witnessed considerable progress due to
the powerful features learned by convolutional neural networks (CNNs). The
current leading approaches for semantic segmentation exploit shape information
by extracting CNN features from masked image regions. This strategy introduces
artificial boundaries on the images and may impact the quality of the extracted
features. Besides, the operations on the raw image domain require to compute
thousands of networks on a single image, which is time-consuming. In this
paper, we propose to exploit shape information via masking convolutional
features. The proposal segments (e.g., super-pixels) are treated as masks on
the convolutional feature maps. The CNN features of segments are directly
masked out from these maps and used to train classifiers for recognition. We
further propose a joint method to handle objects and "stuff" (e.g., grass, sky,
water) in the same framework. State-of-the-art results are demonstrated on
benchmarks of PASCAL VOC and new PASCAL-CONTEXT, with a compelling
computational speed.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
201
What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing
Driven by new software development processes and testing in clouds, system
and integration testing nowadays tends to produce enormous number of alarms.
Such test alarms lay an almost unbearable burden on software testing engineers
who have to manually analyze the causes of these alarms. The causes are
critical because they decide which stakeholders are responsible to fix the bugs
detected during the testing. In this paper, we present a novel approach that
aims to relieve the burden by automating the procedure. Our approach, called
Cause Analysis Model, exploits information retrieval techniques to efficiently
infer test alarm causes based on test logs. We have developed a prototype and
evaluated our tool on two industrial datasets with more than 14,000 test
alarms. Experiments on the two datasets show that our tool achieves an accuracy
of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by
up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per
cause analysis. Due to the attractive experimental results, our industrial
partner, a leading information and communication technology company in the
world, has deployed the tool and it achieves an average accuracy of 72% after
two months of running, nearly three times more accurate than a previous
strategy based on regular expressions.Comment: 12 page
Home Bias in Knowledge Adoption: Evidence From Location Disclosure in An Online Q&A Community
This study investigates whether and how answerers’ location information can bias the askers’ knowledge adoption decisions in online Q&A communities. Drawing on the theories underlying in-group favoritism, we propose that home bias can exist due to categorization and the expectation of better reciprocity from in-group members. We leverage the location disclosure in an online Q&A community in China as a natural experiment setting to identify home bias in knowledge adoption. We find that askers are more likely to adopt answers provided by answerers in the same location after the location disclosure. Moreover, the moderation/heterogeneity analysis suggests: (1) location information serves as a cue related to credibility, and askers rely less on it when other factors signal the answerers\u27 credibility, and (2) askers are more favorable toward answerers in the same location when adopting an answer is associated with an expectation of better reciprocation
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