論文紹介"DynamicFusion: Reconstruction and Tracking of Non-‐rigid Scenes in Real...Ken Sakurada
CVPR2015(Best Paper Award)の論文紹介
"DynamicFusion: Reconstruction and Tracking of Non-‐rigid Scenes in Real-‐Time"
Richard A. Newcombe, Dieter Fox, Steven M. Seitz
内容に関して何かお気づきになりましたら,スライドに記載されているメールアドレスにご連絡頂けると幸いです
文献紹介:Simpler Is Better: Few-Shot Semantic Segmentation With Classifier Weight...Toru Tamaki
Zhihe Lu, Sen He, Xiatian Zhu, Li Zhang, Yi-Zhe Song, Tao Xiang; Simpler Is Better: Few-Shot Semantic Segmentation With Classifier Weight Transformer, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8741-8750
https://openaccess.thecvf.com/content/ICCV2021/html/Lu_Simpler_Is_Better_Few-Shot_Semantic_Segmentation_With_Classifier_Weight_Transformer_ICCV_2021_paper.html
The SV9100 platform is a new system, with new handsets and new applications to empower your workforce. Built on the back of the award winning SV8100 technology, the SV9100 provides double the system capacity, yet cost effective from 10 to over 800 users
【輪読会】Learning Continuous Image Representation with Local Implicit Image Funct...Deep Learning JP
1. The document discusses a new method for single image super-resolution using local implicit image functions (LIIF) based on implicit neural representations. LIIF allows for arbitrary upsampling scales beyond just integer scales.
2. Key techniques include feature unfolding to enrich latent codes, local ensemble of nearby latent codes to reduce artifacts, and cell decoding conditioned on query pixel coordinates to improve quality at high upsampling scales.
3. Experiments show the method achieves performance on par with MetaSR at trained scales and surpasses MetaSR at untrained scales, and it can generate high resolution images even at a scale of 30x through appropriate cell decoding settings.
論文紹介"DynamicFusion: Reconstruction and Tracking of Non-‐rigid Scenes in Real...Ken Sakurada
CVPR2015(Best Paper Award)の論文紹介
"DynamicFusion: Reconstruction and Tracking of Non-‐rigid Scenes in Real-‐Time"
Richard A. Newcombe, Dieter Fox, Steven M. Seitz
内容に関して何かお気づきになりましたら,スライドに記載されているメールアドレスにご連絡頂けると幸いです
文献紹介:Simpler Is Better: Few-Shot Semantic Segmentation With Classifier Weight...Toru Tamaki
Zhihe Lu, Sen He, Xiatian Zhu, Li Zhang, Yi-Zhe Song, Tao Xiang; Simpler Is Better: Few-Shot Semantic Segmentation With Classifier Weight Transformer, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8741-8750
https://openaccess.thecvf.com/content/ICCV2021/html/Lu_Simpler_Is_Better_Few-Shot_Semantic_Segmentation_With_Classifier_Weight_Transformer_ICCV_2021_paper.html
The SV9100 platform is a new system, with new handsets and new applications to empower your workforce. Built on the back of the award winning SV8100 technology, the SV9100 provides double the system capacity, yet cost effective from 10 to over 800 users
【輪読会】Learning Continuous Image Representation with Local Implicit Image Funct...Deep Learning JP
1. The document discusses a new method for single image super-resolution using local implicit image functions (LIIF) based on implicit neural representations. LIIF allows for arbitrary upsampling scales beyond just integer scales.
2. Key techniques include feature unfolding to enrich latent codes, local ensemble of nearby latent codes to reduce artifacts, and cell decoding conditioned on query pixel coordinates to improve quality at high upsampling scales.
3. Experiments show the method achieves performance on par with MetaSR at trained scales and surpasses MetaSR at untrained scales, and it can generate high resolution images even at a scale of 30x through appropriate cell decoding settings.
文献紹介:Token Shift Transformer for Video ClassificationToru Tamaki
Hao Zhang, Yanbin Hao, Chong-Wah Ngo, Token Shift Transformer for Video Classification, ACM MM '21: Proceedings of the 29th ACM International Conference on MultimediaOctober 2021 Pages 917–925https://doi.org/10.1145/3474085.3475272
http://vireo.cs.cityu.edu.hk/papers/Hao_MM2021.pdf
http://arxiv.org/abs/2108.02432
https://dl.acm.org/doi/abs/10.1145/3474085.3475272
The document summarizes research on simulating hydrogen dispersion using the ADVENTURE_sFlow solver. It describes modeling hydrogen dispersion as an analogy to thermal convection problems. Two models are analyzed: a hallway model and a car garage model. The hallway model analyzes hydrogen dispersion from inlet, door, and roof vents in an empty volume. The car garage model analyzes hydrogen leakage from a fuel cell car in a full-scale garage. The objective is to demonstrate the feasibility of using the ADVENTURE_sFlow solver, which uses a hierarchical domain decomposition method, to efficiently solve large-scale problems like hydrogen dispersion in engineering facilities.
14. 有限要素方程式
.pgrad,A
,A,JA,pgardArot,Arot
*
hh
*
h
*
hh
*
hhh
0
Ah : Finite element approximation of A by
the Nedelec elements of simplex type,
ph : Finite element approximation of p by
the conventional piecewise linear
tetrahedral elements,
Ah
* , ph
* : test functions.
16. 非線形反復
Newton法
Picardの逐次近似法
*
hh
*
k
n
h
n
h A,J
~
Arot,Arot 1
*
h
n
h
n
h
n
*
hh
*
h
n
h
n
h
n
*
h
n
h
n
h
Arot,ArotA
A
A,J
~
Arot,ArotA
A
Arot,Arot
11
32. 有限要素方程式
.0)grad,()grad,grad(
),,
~
(),grad(
),()rot,rot(
**
**
**
hhhh
hhhh
hhhh
Ai
AJA
AAiAA
Ah : Finite element approximation of A by
the Nedelec elements of simplex type,
h : Finite element approximation of by
the conventional piecewise linear
tetrahedral elements,
Ah
* , h
* : test functions,
( . , . ) : the complex valued L2-inner product.
J : an excitation current density
[A/m2] (div J = 0 in Ω)
: the angular frequency [rad/s],
: the magnetic reluctivity [m/H],
: the conductivity [S/m],
i : the imaginary unit.
33. A法
3次元時間調和渦電流問題
W: 多面体領域
R: 導体領域
S: 不導体領域
未知関数(複素関数)
A: 磁気ベクトルポテンシャル [Wb/m]
∂W
R
S
Γ
W
n
35. 有限要素方程式
Ah : Finite element approximation of A by
the Nedelec elements of simplex type,
Ah
* : test function,
( . , . ) : the complex valued L2-inner product.
J : an excitation current density
[A/m2] (div J = 0 in Ω)
: the angular frequency [rad/s],
: the magnetic reluctivity [m/H],
: the conductivity [S/m],
i : the imaginary unit.
.,
~
,, ***
hhhhhh AJAAiArotArot
40. インターフェース問題
領域間境界上自由度について
反復法で解く
部分領域内部自由度について
行列シュアコンプリメント:
1
N
i
Ti
B
ii
B RSRS
リメント行列ローカルシュアコンプ
†
:
i
IB
i
II
Ti
IB
i
BB
i
KKKKS
gSuB
NiuKfuK i
B
i
IB
i
I
i
I
i
II ,,1,
42. 反復型領域分割法
COCG法(Conjugate Orthogonal Conjugate Gradient method)
;end
;
;
;break,If
;
;
;
,.....;1,0for
;
;Choose
11
11
01
1
1
00
00
0
nnnn
nTnnTnn
n
nnnn
nnn
B
n
B
nTnnTnn
nn
B
B
prp
rrrr
rr
qrr
puu
qprr
Spq
n
rp
gSur
u
b
b
d
43. 反復型領域分割法
COCG法(Conjugate Orthogonal Conjugate Gradient method)
;
;
;
byCompute
subdomaineachIn
;Choose
00
1
00
000
00
0
0
rp
rRr
fuRKuKr
uRKfuK
u
u
N
i
ii
B
i
BB
Ti
B
i
BB
i
I
Ti
IB
i
B
Ti
B
i
IB
i
I
i
I
i
II
i
I
B
;end
;
;
;break,If
;
;
;
;
;
;
byCompute
subdomaineachIn
,.....;1,0for
11
11
01
1
1
1
nnnn
nTnnTnn
n
nnnn
nnn
B
n
B
nTnnTnn
N
i
nii
B
n
nTi
B
i
BB
ni
I
Ti
IB
ni
nTi
B
i
IB
ni
I
i
II
ni
I
prp
rrrr
rr
qrr
puu
qprr
qRq
pRKpKq
pRKpK
p
n
b
b
d
□の部分は各部分領域で独立に計算
可能であり,並列化が容易
この部分の計算が全体の9割以上を占
めるため,高い並列化効率を得やすい
46. 反復型領域分割法の並列化
反復型領域分割法の並列性
部分領域演算は並列に処理可能
階層型領域分割法
データの階層構造
部分(part)-部分領域(subdomain)
構造解析において1億自由度規模問題解析の実績
R. Shioya and G. Yagawa, Parallel finite elements of ten-
million DOFs based on domain decomposition method,
WCCM IV Computational Mechanics-New Trends and
Applications-IV, 11, pp.1-12, 1998.