Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
一般物体検出とLSTMを用いた画像に基づく屋内位置推定 - IPSJ UBI82
Search
Aokiti
May 12, 2024
520
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
一般物体検出とLSTMを用いた画像に基づく屋内位置推定 - IPSJ UBI82
http://id.nii.ac.jp/1001/00233750/
Aokiti
May 12, 2024
More Decks by Aokiti
See All by Aokiti
d-hacks 今期運営 2025f
sakusaku3939
0
670
[d-hacks Docker講座] Dockerで動かすローカルLLM入門
sakusaku3939
0
110
[論文輪読会] A survey of model compression strategies for object detection
sakusaku3939
0
44
[論文輪読会] ViT-1.58b
sakusaku3939
0
330
d-hacks PyTorchモデル実装会 2024f
sakusaku3939
0
63
[論文輪読会] Binarized Neural Networks
sakusaku3939
0
73
MoodTune 東京AI祭ハッカソン決勝
sakusaku3939
0
640
d-hacks PyTorch実装会 2023f
sakusaku3939
0
32
[DL勉強会] 第5章 ディープラーニングを活用したアプリケーション 後半
sakusaku3939
0
24
Featured
See All Featured
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
35
3.8k
Why Your Marketing Sucks and What You Can Do About It - Sophie Logan
marketingsoph
0
410
Highjacked: Video Game Concept Design
rkendrick25
PRO
1
460
What the history of the web can teach us about the future of AI
inesmontani
PRO
1
700
Rails Girls Zürich Keynote
gr2m
96
14k
Reality Check: Gamification 10 Years Later
codingconduct
0
2.3k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
47
8.3k
For a Future-Friendly Web
brad_frost
183
10k
Building AI with AI
inesmontani
PRO
1
1.2k
We Analyzed 250 Million AI Search Results: Here's What I Found
joshbly
1
2k
Jess Joyce - The Pitfalls of Following Frameworks
techseoconnect
PRO
1
420
BBQ
matthewcrist
89
10k
Transcript
1
2
3
4
5
6
[7] F. Walch, C. Hazirbas, L. Leal-Taixé, T. Sattler, S.
Hilsenbeck, D. Cremers 7 https://doi.org/10.1109/ICCV.2017.75
8 [8] S Nilwong, D Hossain, S Kaneko, G Capi
https://doi.org/10.3390/machines7020025
9
10 CNN(GoogLeNetモデル) LSTM(次元を削減) CNN CNN CNN LSTM(1つの特徴量に変換)
11 CNN CNN CNN LSTM(1つの特徴量に変換)
12
13
14
15
16 𝑀𝐴𝐸 = 1 𝑛 𝑖=1 𝑛 |ෝ 𝑥𝑖
− 𝑥𝑖 + | ෝ 𝑦𝑖 − 𝑦𝑖 |)
17 CNN (4層) CNN(4層) CNN(4層) LSTM(1つの特徴量に変換) CNN(4層) CNN(GoogLeNet) CNN(GoogLeNet) LSTM(次元を削減)
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34