32 research outputs found
A World of Difference: Divergent Word Interpretations among People
Divergent word usages reflect differences among people. In this paper, we
present a novel angle for studying word usage divergence -- word
interpretations. We propose an approach that quantifies semantic differences in
interpretations among different groups of people. The effectiveness of our
approach is validated by quantitative evaluations. Experiment results indicate
that divergences in word interpretations exist. We further apply the approach
to two well studied types of differences between people -- gender and region.
The detected words with divergent interpretations reveal the unique features of
specific groups of people. For gender, we discover that certain different
interests, social attitudes, and characters between males and females are
reflected in their divergent interpretations of many words. For region, we find
that specific interpretations of certain words reveal the geographical and
cultural features of different regions.Comment: 4 pages, 1 figure, published at ICWSM'1
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Convolutional neural networks have been widely deployed in various
application scenarios. In order to extend the applications' boundaries to some
accuracy-crucial domains, researchers have been investigating approaches to
boost accuracy through either deeper or wider network structures, which brings
with them the exponential increment of the computational and storage cost,
delaying the responding time. In this paper, we propose a general training
framework named self distillation, which notably enhances the performance
(accuracy) of convolutional neural networks through shrinking the size of the
network rather than aggrandizing it. Different from traditional knowledge
distillation - a knowledge transformation methodology among networks, which
forces student neural networks to approximate the softmax layer outputs of
pre-trained teacher neural networks, the proposed self distillation framework
distills knowledge within network itself. The networks are firstly divided into
several sections. Then the knowledge in the deeper portion of the networks is
squeezed into the shallow ones. Experiments further prove the generalization of
the proposed self distillation framework: enhancement of accuracy at average
level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as
maximum. In addition, it can also provide flexibility of depth-wise scalable
inference on resource-limited edge devices.Our codes will be released on github
soon.Comment: 10page
Neural Simile Recognition with Cyclic Multitask Learning and Local Attention
Simile recognition is to detect simile sentences and to extract simile
components, i.e., tenors and vehicles. It involves two subtasks: {\it simile
sentence classification} and {\it simile component extraction}. Recent work has
shown that standard multitask learning is effective for Chinese simile
recognition, but it is still uncertain whether the mutual effects between the
subtasks have been well captured by simple parameter sharing. We propose a
novel cyclic multitask learning framework for neural simile recognition, which
stacks the subtasks and makes them into a loop by connecting the last to the
first. It iteratively performs each subtask, taking the outputs of the previous
subtask as additional inputs to the current one, so that the interdependence
between the subtasks can be better explored. Extensive experiments show that
our framework significantly outperforms the current state-of-the-art model and
our carefully designed baselines, and the gains are still remarkable using
BERT.Comment: AAAI 202
The relationship between fundamental motor skills and physical fitness in preschoolers: a short-term longitudinal study
PurposePhysical fitness and fundamental motor skills are two important aspects for the healthy development of preschoolers. Despite the growing interest in clarifying their relationship, the scarcity of longitudinal studies prevents us from understanding causality.MethodThis study employed a cross-lagged model with two time points to investigate the bidirectional relationship between these two aspects. A total of 174 preschoolers (54.0% girls) from 3 to 6 years old (M = 3.96 ± 0.47) were surveyed, they were recruited by convenience from two kindergartens in Beijing, China, and their physical fitness (via CNPFDSM-EC) and fundamental motor skills (via TGMD-3) were tracked over a period of 6 months.ResultsThe findings revealed a bidirectional predictive effect. The predictive strength of flexibility was found to be lower than other physical fitness aspects, while locomotor skills demonstrated a higher predictive strength than object control skills.ConclusionThis study indicates that physical fitness and fundamental motor skills mutually enhance each other in young children, and both should be emphasized in preschool sports education
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
Object detection is an important and challenging problem in computer vision.
Although the past decade has witnessed major advances in object detection in
natural scenes, such successes have been slow to aerial imagery, not only
because of the huge variation in the scale, orientation and shape of the object
instances on the earth's surface, but also due to the scarcity of
well-annotated datasets of objects in aerial scenes. To advance object
detection research in Earth Vision, also known as Earth Observation and Remote
Sensing, we introduce a large-scale Dataset for Object deTection in Aerial
images (DOTA). To this end, we collect aerial images from different
sensors and platforms. Each image is of the size about 4000-by-4000 pixels and
contains objects exhibiting a wide variety of scales, orientations, and shapes.
These DOTA images are then annotated by experts in aerial image interpretation
using common object categories. The fully annotated DOTA images contains
instances, each of which is labeled by an arbitrary (8 d.o.f.)
quadrilateral To build a baseline for object detection in Earth Vision, we
evaluate state-of-the-art object detection algorithms on DOTA. Experiments
demonstrate that DOTA well represents real Earth Vision applications and are
quite challenging.Comment: Accepted to CVPR 201