629 research outputs found
Multi-task CNN Model for Attribute Prediction
This paper proposes a joint multi-task learning algorithm to better predict
attributes in images using deep convolutional neural networks (CNN). We
consider learning binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multi-task learning
allows CNN models to simultaneously share visual knowledge among different
attribute categories. Each CNN will generate attribute-specific feature
representations, and then we apply multi-task learning on the features to
predict their attributes. In our multi-task framework, we propose a method to
decompose the overall model's parameters into a latent task matrix and
combination matrix. Furthermore, under-sampled classifiers can leverage shared
statistics from other classifiers to improve their performance. Natural
grouping of attributes is applied such that attributes in the same group are
encouraged to share more knowledge. Meanwhile, attributes in different groups
will generally compete with each other, and consequently share less knowledge.
We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape
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Solid Freeform Fabrication of Artificial Human Teeth
In this paper, we describe a solid freeform fabrication procedure for human dental
restoration via porcelain slurry micro-extrusion. Based on submicron-sized dental porcelain
powder obtained via ball milling process, a porcelain slurry formulation has been developed. The
formulation developed allows the porcelain slurry to show a pseudoplastic behavior and
moderate viscosity, which permits the slurry to re-shape to form a near rectangular cross section.
A well-controlled cross-section geometry of the extrudate is important for micro-extrusion to
obtain uniform 2-D planes and for the addition of the sequential layers to form a 3-D object.
Human teeth are restored by this method directly from CAD digital models. After sintering,
shrinkage of the artificial teeth is uniform in all directions. Microstructure of the sintered teeth is
identical to that made via traditional dental restoration processes.Mechanical Engineerin
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Laser Densification of Extruded Dental Porcelain Bodies in Multi-Material Laser Densification (MMLD) Process
In this study commercial dental porcelain powder was deposited via slurry extrusion and
laser densified to fabricate dental restorations in a Multi-Material Laser Densification (MMLD)
process. The processing conditions for laser densification of single lines and closed rings were
investigated in order to avoid warping and cracking. Multi-layer rings were also investigated to
study the dependence of bonding between layers on the laser densification conditions. The laser
densified rings showed no warping, and good bonding between layers could be achieved when
the laser densification condition was selected properly. The mechanism to achieve porcelain
rings without warping and cracking is discussed. The understanding developed will pave the way
for fabricating a physical dental restoration unit.Mechanical Engineerin
Bilingual sentence alignment of pre-Qin history literature for digital humanities study
Sentence aligned bilingual text of history literature provides support of digital resources for related digital humanities studies, but existing studies have done little work on sentence alignment of ancient Chinese and English. In this study, we made a preliminary attempt to align the sentence of ancient Chinese and English. We used the bilingual text of the Analects of Confucius and Zuo's Commentaries of the Spring and Autumn Annals, extracted features and adopted the classification method to divide the bilingual candidate sentence pairs based on probability scores. The bilingual sentence alignment model based on SVM had the best performance on a larger amount of data when using three features and confirmed the impact of candidate dataset
GAN Prior based Null-Space Learning for Consistent Super-Resolution
Consistency and realness have always been the two critical issues of image
super-resolution. While the realness has been dramatically improved with the
use of GAN prior, the state-of-the-art methods still suffer inconsistencies in
local structures and colors (e.g., tooth and eyes). In this paper, we show that
these inconsistencies can be analytically eliminated by learning only the
null-space component while fixing the range-space part. Further, we design a
pooling-based decomposition (PD), a universal range-null space decomposition
for super-resolution tasks, which is concise, fast, and parameter-free. PD can
be easily applied to state-of-the-art GAN Prior based SR methods to eliminate
their inconsistencies, neither compromising the realness nor bringing extra
parameters or computational costs. Besides, our ablation studies reveal that PD
can replace pixel-wise losses for training and achieve better generalization
performance when facing unseen downsamplings or even real-world degradation.
Experiments show that the use of PD refreshes state-of-the-art SR performance
and speeds up the convergence of training up to 2~10 times.Comment: Accepted by AAAI 202
Introspective Deep Metric Learning for Image Retrieval
This paper proposes an introspective deep metric learning (IDML) framework
for uncertainty-aware comparisons of images. Conventional deep metric learning
methods produce confident semantic distances between images regardless of the
uncertainty level. However, we argue that a good similarity model should
consider the semantic discrepancies with caution to better deal with ambiguous
images for more robust training. To achieve this, we propose to represent an
image using not only a semantic embedding but also an accompanying uncertainty
embedding, which describes the semantic characteristics and ambiguity of an
image, respectively. We further propose an introspective similarity metric to
make similarity judgments between images considering both their semantic
differences and ambiguities. The proposed IDML framework improves the
performance of deep metric learning through uncertainty modeling and attains
state-of-the-art results on the widely used CUB-200-2011, Cars196, and Stanford
Online Products datasets for image retrieval and clustering. We further provide
an in-depth analysis of our framework to demonstrate the effectiveness and
reliability of IDML. Code is available at: https://github.com/wzzheng/IDML.Comment: The extended version of this paper is accepted to T-PAMI. Source code
available at https://github.com/wzzheng/IDM
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