126 research outputs found
Trade Remedies: The Impact on the Proposed Australia-China Free Trade Agreement
Article published in the Michigan State International Law Review
Trade Remedies: The Impact on the Proposed Australia-China Free Trade Agreement
Article published in the Michigan State International Law Review
A Cross-Cultural Study on Teachers’ Use of Print and Digital Resources in Sweden, Finland, the USA, and Flanders : Some Methodological Challenges
Cross-cultural studies have inherent challenges as researchers from different cultural backgrounds attempt to make sense of similar-seeming material in unfamiliar contexts and communicate seemingly-obvious aspects of their own culture to outsiders (Clarke, 2013; Osborn, 2004). This contribution explores some of the methodological challenges in a cross-cultural study on teachers’ use of print and digital resources in four regions: Sweden, Finland, the USA, and Flanders (Belgium). All but one of the seven team members are insiders to one of the four contexts and to different extents outsiders to the other contexts. In order to benefit from insider-outsider perspectives, we designed five tools to develop alignment of insider and outsider lenses. We describe these tools in this contribution.Peer reviewe
Take a Prior from Other Tasks for Severe Blur Removal
Recovering clear structures from severely blurry inputs is a challenging
problem due to the large movements between the camera and the scene. Although
some works apply segmentation maps on human face images for deblurring, they
cannot handle natural scenes because objects and degradation are more complex,
and inaccurate segmentation maps lead to a loss of details. For general scene
deblurring, the feature space of the blurry image and corresponding sharp image
under the high-level vision task is closer, which inspires us to rely on other
tasks (e.g. classification) to learn a comprehensive prior in severe blur
removal cases. We propose a cross-level feature learning strategy based on
knowledge distillation to learn the priors, which include global contexts and
sharp local structures for recovering potential details. In addition, we
propose a semantic prior embedding layer with multi-level aggregation and
semantic attention transformation to integrate the priors effectively. We
introduce the proposed priors to various models, including the UNet and other
mainstream deblurring baselines, leading to better performance on severe blur
removal. Extensive experiments on natural image deblurring benchmarks and
real-world images, such as GoPro and RealBlur datasets, demonstrate our
method's effectiveness and generalization ability
Mathematics curriculum reform in the U.S., Finland, Sweden and Flanders : region-wide coherence versus teacher involvement?
Peer reviewe
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
Multi-frame depth estimation generally achieves high accuracy relying on the
multi-view geometric consistency. When applied in dynamic scenes, e.g.,
autonomous driving, this consistency is usually violated in the dynamic areas,
leading to corrupted estimations. Many multi-frame methods handle dynamic areas
by identifying them with explicit masks and compensating the multi-view cues
with monocular cues represented as local monocular depth or features. The
improvements are limited due to the uncontrolled quality of the masks and the
underutilized benefits of the fusion of the two types of cues. In this paper,
we propose a novel method to learn to fuse the multi-view and monocular cues
encoded as volumes without needing the heuristically crafted masks. As unveiled
in our analyses, the multi-view cues capture more accurate geometric
information in static areas, and the monocular cues capture more useful
contexts in dynamic areas. To let the geometric perception learned from
multi-view cues in static areas propagate to the monocular representation in
dynamic areas and let monocular cues enhance the representation of multi-view
cost volume, we propose a cross-cue fusion (CCF) module, which includes the
cross-cue attention (CCA) to encode the spatially non-local relative
intra-relations from each source to enhance the representation of the other.
Experiments on real-world datasets prove the significant effectiveness and
generalization ability of the proposed method.Comment: Accepted by CVPR 2023. Code and models are available at:
https://github.com/ruili3/dynamic-multiframe-dept
Going the Extra Mile in Face Image Quality Assessment:A Novel Database and Model
An accurate computational model for image quality assessment (IQA) benefits
many vision applications, such as image filtering, image processing, and image
generation. Although the study of face images is an important subfield in
computer vision research, the lack of face IQA data and models limits the
precision of current IQA metrics on face image processing tasks such as face
superresolution, face enhancement, and face editing. To narrow this gap, in
this paper, we first introduce the largest annotated IQA database developed to
date, which contains 20,000 human faces -- an order of magnitude larger than
all existing rated datasets of faces -- of diverse individuals in highly varied
circumstances. Based on the database, we further propose a novel deep learning
model to accurately predict face image quality, which, for the first time,
explores the use of generative priors for IQA. By taking advantage of rich
statistics encoded in well pretrained off-the-shelf generative models, we
obtain generative prior information and use it as latent references to
facilitate blind IQA. The experimental results demonstrate both the value of
the proposed dataset for face IQA and the superior performance of the proposed
model.Comment: Appearing in IEEE TM
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