20,950 research outputs found
Extracting News Events from Microblogs
Twitter stream has become a large source of information for many people, but
the magnitude of tweets and the noisy nature of its content have made
harvesting the knowledge from Twitter a challenging task for researchers for a
long time. Aiming at overcoming some of the main challenges of extracting the
hidden information from tweet streams, this work proposes a new approach for
real-time detection of news events from the Twitter stream. We divide our
approach into three steps. The first step is to use a neural network or deep
learning to detect news-relevant tweets from the stream. The second step is to
apply a novel streaming data clustering algorithm to the detected news tweets
to form news events. The third and final step is to rank the detected events
based on the size of the event clusters and growth speed of the tweet
frequencies. We evaluate the proposed system on a large, publicly available
corpus of annotated news events from Twitter. As part of the evaluation, we
compare our approach with a related state-of-the-art solution. Overall, our
experiments and user-based evaluation show that our approach on detecting
current (real) news events delivers a state-of-the-art performance
Object Detection based on Region Decomposition and Assembly
Region-based object detection infers object regions for one or more
categories in an image. Due to the recent advances in deep learning and region
proposal methods, object detectors based on convolutional neural networks
(CNNs) have been flourishing and provided the promising detection results.
However, the detection accuracy is degraded often because of the low
discriminability of object CNN features caused by occlusions and inaccurate
region proposals. In this paper, we therefore propose a region decomposition
and assembly detector (R-DAD) for more accurate object detection.
In the proposed R-DAD, we first decompose an object region into multiple
small regions. To capture an entire appearance and part details of the object
jointly, we extract CNN features within the whole object region and decomposed
regions. We then learn the semantic relations between the object and its parts
by combining the multi-region features stage by stage with region assembly
blocks, and use the combined and high-level semantic features for the object
classification and localization. In addition, for more accurate region
proposals, we propose a multi-scale proposal layer that can generate object
proposals of various scales. We integrate the R-DAD into several feature
extractors, and prove the distinct performance improvement on PASCAL07/12 and
MSCOCO18 compared to the recent convolutional detectors.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligence (AAAI
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