computervisionblog.com

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Title

Tombone's Computer Vision Blog

Description

A Blog about Deep Learning, Computer Vision, and the algorithms that are shaping the future of Artificial Intelligence.

Keywords

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utf-8

No	Meta Name	Value
1	viewport	width=1100

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Type	Status
HTML 5
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Text / Code Ratio 21.54 %

computervisionblog.com has a website text/code ratio of 21.54 %. Search engine crawlers tend to not pick up pages with inadequate content.

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Titles

H11
H212
H38
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H60

No	Text
1	Tombone's computer vision blog

No	Text
1	Tuesday, november 19, 2019
2	Wednesday, may 16, 2018
3	Friday, december 16, 2016
4	Friday, june 17, 2016
5	Pop****r posts
6	Recent posts
7	About me
8	Links
9	Blog archive
10	Labels
11	Follow by email
12	Subscribe to

No	Text
1	Computer vision and visual slam vs. aı agents
2	D*******s: aı-powered deception machines
3	Nuts and bolts of building deep learning applications: ng @ nıps2016
4	Making deep networks probabilistic via test-time dropout
5	How to use dropout: 2016 edition
6	Bayesian convolutional neural networks
7	Theoretical bayesian deep learning
8	The gaussian process: a machine learning approach with built-in uncertainty modeling

Text Styling

STRONG14
B54
EM6
I10
U0
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STRONG

No	Text
1	I. Does Computer Vision Matter for Action?
2	Probably not.
3	yes
4	II. Visual SLAM for AI Agents
5	III.
6	Quō vādis Visual SLAM? Trends and research forecast
7	Conclusion
8	References:
9	Vladlen Koltun.
10	Direct Spa**** Odometry.
11	Does Computer Vision Matter for Action?
12	Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
13	gradSLAM: Dense SLAM meets Automatic Differentiation.
14	Emergent tool use from multi-agent autocurricula.

No	Text
1	Video describing Koltun's Does Computer Vision Matter for Action? [3]
2	Teaser Image for the 2nd Workshop on Deep Learning for Visual SLAM from Ronnie Clark.See info at http://visualslam.ai
3	Key Figure from the gradSLAM paper on end-to-end learning for SLAM. [5]
4	Adrien Gaidon's talk from October 11th, 2019 on Self-Supervised Learning in the context of Autonomous Cars
5	A presentation about self-supervision from Alyosha Efros on May 25th, 2018
6	Video from OpenAI showing Multi-Agent Hide and Seek.
7	pip install DeepSLAM
8	SuperPoint: Self-supervised interest point detection and description.
9	Deep Fakes.
10	From the DeepDream phenomenon of 2015 to the Deep Style Transfer Art apps of 2016, 2018 is the year of the d*******.
11	What is a Deep Fake?
12	How are Deep Fakes made?
13	Face2Face technique from 2016
14	Ira's early work on face swapping in 2010.
15	Ira's Dreambit system
16	Transfiguring Portraits
17	Fighting against d*******s
18	The "fakeness" masks in FaceForensics[3] are based on XceptionNet
19	Fighting Fake News: Image Splice Detection
20	.
21	Concluding Remarks
22	References
23	rigor
24	Figure 1.
25	Andrew Ng and the Lecture
26	Money-making deep learning vs Idea-generating deep learning
27	"It takes surprisingly long time to grok bias and variance deeply, but people that understand bias and variance deeply are often able to drive very rapid progress."
28	The 5-step method of building better systems
29	Figure 2.
30	Figure 3.
31	Conclusion
32	Appendix
33	position
34	momentum
35	interpretability
36	performance
37	Interpretability vs Performance:
38	Dropout
39	dropout
40	dropout used attest-timeis one of the simplest ways to turn raw neural network outputs into probability distributions
41	How to use dropout: 2016 edition
42	Bayesian Convolutional Neural Networks
43	w (3)
44	X
45	Y
46	y (2)
47	x
48	Bayesian SegNet.
49	Theoretical Bayesian Deep Learning
50	The Gaussian Process:
51	Related Posts

No	Text
1	"What do Artificial Intelligence Agents need (if anything) from the field of Computer Vision?"
2	Probably not.
3	yes
4	"Will a single Visual SLAM framework rule them all?"
5	arXiv (2)

No	Text
1	The ability to effortlessly create visually plausible editing of faces in videos has the potential to severely undermine trust in any form of digital communication.
2	As new advances in computer vision and image-editing emerge, there is an increasingly urgent need for effective visual forensics methods. We see our approach, which successfully detects manip**tions without seeing examples of manip**ted images, as being an initial step toward building general-purpose forensics tools
3	rigor
4	The Nuts and Bolts of Building Applications using Deep Learning
5	CEO-style
6	Professor-style
7	how do you make your deep learning systems better
8	--Andrew Ng
9	Andrew Ng the Arnold Schwarzenegger of Machine Learning
10	always

LINK ANALYSIS

Total Link Count: 452

Internal Link Count : 344

No	Text	Type
1	Computer Vision and Visual SLAM vs. AI Agents	text
2	Tuesday, November 19, 2019	text
3	2 comments:	text
4	action	text
5	adrien gaidon	text
6	AI agents	text
7	angela dai	text
8	autonomous cars	text
9	computer vision	text
10	conference	text
11	daniel cremers	text
12	deep learning	text
13	gradslam	text
14	iccv 2019	text
15	kornia	text
16	panel	text
17	research	text
18	victor prisacariu	text
19	visual slam	text
20	vladlen koltun	text
21	-	empty
22	d*******s: AI-powered deception machines	text
23	Wednesday, May 16, 2018	text
24	No comments:	text
25	alyosha efros	text
26	cvpr	text
27	d*******	text
28	descartes	text
29	face detection	text
30	face transfer	text
31	face2face	text
32	fake news	text
33	GANs	text
34	Ira Kemelmacher-Shlizerman	text
35	justus thies	text
36	matthias niessner	text
37	realism	text
38	siggraph	text
39	snapchat	text
40	truth	text
41	visual forgery	text
42	-	empty
43	Nuts and Bolts of Building Deep Learning Applications: Ng @ NIPS2016	text
44	Friday, December 16, 2016	text
45	No comments:	text
46	advice	text
47	andrew ng	text
48	bias-variance	text
49	deep learning	text
50	google	text

External Link Count : 108

No	Text	Type
1	-	image
2	Vladlen Koltun	text
3	Direct Spa**** Odometry (DSO) system	text
4	Does Computer Vision Matter for Action?	text
5	Workshop on Deep Learning for Visual SLAM at ICCV 2019	text
6	Victor Prisacariu	text
7	6d.ai	text
8	Daniel Cremers	text
9	ArtiSense.ai	text
10	Angela Dai	text
11	Vladlen Koltun	text
12	Tomasz Malisiewicz	text
13	-	image
14	Ronnie Clark	text
15	http://visualslam.ai	text
16	Kornia	text
17	gradSLAM	text
18	Krishna	text
19	Murthy	text
20	-	image
21	gradSLAM	text
22	SuperPoint	text
23	Adrien Gaidon	text
24	Alyosha Efros	text
25	multi-agents from OpenAI	text
26	http://vladlen.info/	text
27	http://vladlen.info/publications/direct-spa****-odometry/	text
28	http://vladlen.info/publications/computer-vision-matter-action/	text
29	https://kornia.github.io/	text
30	http://montrealrobotics.ca/gradSLAM/	text
31	https://openai.com/blog/emergent-tool-use/	text
32	https://arxiv.org/abs/1712.07629	text
33	Tomasz Malisiewicz	text
34	-	image
35	-	image
36	Email This	text
37	BlogThis!	text
38	Share to Twitter	text
39	Share to Facebook	text
40	Share to Pinterest	text
41	-	image
42	-	image
43	Ira Kemelmacher-Shlizerman	text
44	-	image
45	-	image
46	-	image
47	-	image
48	Face2face: Real-time face capture and reenactment of rgb videos	text
49	Being john malkovich	text
50	FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces	text
51	Fighting Fake News: Image Splice Detection via Learned Self-Consistency.	text
52	Transfiguring portraits	text
53	Tomasz Malisiewicz	text
54	-	image
55	-	image
56	Email This	text
57	BlogThis!	text
58	Share to Twitter	text
59	Share to Facebook	text
60	Share to Pinterest	text
61	-	image
62	-	image
63	-	image
64	Andrew Ng Nuts and Bolts of Applying Deep Learning Lecture on YouTube	text
65	Kevin Zakka's blog post	text
66	Tomasz Malisiewicz	text
67	-	image
68	-	image
69	Email This	text
70	BlogThis!	text
71	Share to Twitter	text
72	Share to Facebook	text
73	Share to Pinterest	text
74	-	image
75	-	image
76	-	empty
77	-	image
78	Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding	text
79	project page with videos	text
80	-	image
81	Yarin Gal	text
82	Zoubin Ghahramani	text
83	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	text
84	Appendix	text
85	A Theoretically Grounded Application of Dropout in Recurrent Neural Networks	text
86	What My Deep Model Doesn't Know	text
87	Homoscedastic and Heteroscedastic Regression with Dropout Uncertainty	text
88	-	image
89	Tomasz Malisiewicz	text
90	-	image
91	-	image
92	Email This	text
93	BlogThis!	text
94	Share to Twitter	text
95	Share to Facebook	text
96	Share to Pinterest	text
97	Recent Posts Widget	text
98	Tomasz Malisiewicz	text
99	View my complete profile	text
100	Tomasz @ MIT Research Homepage	text
101	Tomasz @ Google Scholar Citations	text
102	Tomasz @ Github Open-Source Code	text
103	-	image
104	-	image
105	-	image
106	-	image
107	-	image
108	Blogger	text

Nofollow Link Count : 11

No	Text	Type
1	-	image
2	Recent Posts Widget	text
3	-	image
4	-	image
5	-	image
6	-	image
7	-	image
8	-	image
9	-	image
10	-	image
11	-	image

Title Link Count : 0

WEBSITE SERVER INFORMATION

Service Provider (ISP)
Namecheap
Hosted IP Address
192.64.119.194
Hosted Country
United States
Host Region
New Jersey , Newark
Latitude and Longitude
40.7357 : -74.1724

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