๐Ÿข ETRI (2024.01.05)

๐Ÿ“– ๊ณต๋ถ€! (9:00-12:00)

๋จผ์ € ์ฝ์„ ๋…ผ๋ฌธ ๋ฆฌ์ŠคํŠธ๋ถ€ํ„ฐ ๋ฝ‘์•„๋ณด์•˜๋‹ค.

โˆ™ Unsupervised Domain Adaptation of Object Detectors: A Survey (IEEE 2023)
โˆ™ Deep Domain Adaptive Object Detection: a Survey (IEEE 2020)

โˆ™ Domain Adaptive Faster R-CNN for Object Detection in the Wild (CVPR 2018)
โˆ™ Multi-Source Domain Adaptation for Object Detection (ICCV 2021)
โˆ™ Multi-Granularity Alignment Domain Adaptation for Object Detection (CVPR 2022)
โˆ™ Progressive Domain Adaptation for Object Detection (WACV 2020)
โˆ™ SimROD: A Simple Adaptation Method for Robust Object Detection (ICCV 2021) 
โˆ™ ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing (WACV 2023)
โˆ™ Domain Contrast for Domain Adaptive Object Detection (IEEE 2021) 
โˆ™ Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation (ACCV 2020)
โˆ™ One-Shot Unsupervised Domain Adaptation for Object Detection (IEEE 2020)
โˆ™ A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data (AAAI 2021)
โˆ™ Object detection based on semi-supervised domain adaptation for imbalanced domain resources (Machine Vision and Applications 31 2020)
โˆ™ Cross-domain adaptive teacher for object detection (CVPR 2022)

 

 

 

 

 

 

 

 

 

 

 

๐Ÿš ๋ฐฅ์ด๋‹ค ๋ฐฅ!

๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๋™ํŽธ์ œ(ํ•œ์‹)... ETRIํ‘œ ๋–ก๊ฐˆ๋น„ ์ง„์งœ ๋ง›์žˆ์—ˆ๋‹ค!!(์‚ฌ์‹ค ๊ฒฝ์–‘์‹ ๋ถˆ๊ณ ๊ธฐ? ๋Š๋‚Œ์ด์—ˆ์ง€๋งŒ)

 

 
 
 
 
 

๐Ÿ› ๏ธ ์„œ๋ฒ„ ์ž‘์—…! (14:00-18:00) 

 

 


GPU ๋„ฃ๊ณ  ์ƒˆ๋กœ OS(Linux) ๊น”๊ณ  ์ž‘์—…ํ•˜๋Š”๋ฐ ํž˜๋“ค์–ด ์ฃฝ๋Š”์ค„ใ… ใ… 

GUI์— ๋ฌธ์ œ๊ฐ€ ์žˆ๋Š” ์„œ๋ฒ„, Linux ์ ‘์†์ด ์•ˆ๋˜๋Š” ์„œ๋ฒ„ ๋“ฑ๋“ฑ GPU๋„ ์žฅ์ฐฉํ•˜๊ณ  ์•ฝ๊ฐ„ ๋ฐ‘๋ฐ”๋‹ฅ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•˜๋Š” ๋Š๋‚Œ์ด์–ด์„œ ์ƒˆ๋กญ๊ธฐ๋„ ํ–ˆ๊ณ , ๊ทธ๋™์•ˆ ์ด๋ฏธ ๋งŒ๋“ค์–ด์ง„ ์„œ๋ฒ„๋กœ ๐Ÿฏ๋นจ๊ณ  ์žˆ์—ˆ๋˜ ๊ฒƒ ๊ฐ™์•„์„œ ์ข€ ๋ฐ˜์„ฑ(?)๋„ ๋˜์—ˆ๋˜ ์‹œ๊ฐ„์ด์—ˆ๋‹คใ…Žใ…Ž


 

 

 

 

 

 


 
 

๐Ÿš ๋ฐฅ์ด๋‹ค ๋ฐฅ!

ETRI๋™๊ธฐ๋ถ„๋“ค์ด๋ž‘ ๊ฐ„๋‹จํ•œ? ์‹์‚ฌ๋ฅผ ๊ฐ€์กŒ๋‹ค.

๋ฏธ์†Œ์•ผ๋ผ๋Š” ๊ณณ์—์„œ ๋จน์—ˆ์—ˆ๋Š”๋ฐ ์ด 12๋ช…(๋‚˜ํฌํ•จ)์ด ์‹๋‹น์— ๋“ค์–ด์„œ๋‹ˆ ํ•œ ๊ณต๊ฐ„์„ ๋‹ค ์ฐจ์ง€ํ•ด ๋ฒ„๋ ธ๋‹ค.

์‹์‚ฌ๋กœ๋Š” ์šฐ์‚ผ๊ฒธ ๋ฎ๋ฐฅ์„ ๊ณจ๋ž๋‹ค. (์ด๋ฆ„๋งŒ ๋ณด๊ณ  ๊ณจ๋ž๋˜๊ฒŒ ์ง„์งœ์ง„์งœ ์‹ค์ˆ˜์˜€๋‹ค.)

์šฐ์‚ผ๊ฒน ๋ฎ๋ฐฅ์ด๋ž˜์š” ์ด๊ฒŒ...

์šฐ์‚ผ๊ฒธ ๋ฎ๋ฐฅ....์ด๊ฒŒ...?

๋‚ด๊ฐ€ ์•„๋Š” ์šฐ์‚ผ๊ฒน ๋ฎ๋ฐฅ์€ ๊ฐ„์žฅ์†Œ์Šค ๋ฒ ์ด์Šค๋กœ ๋งŒ๋“ค์–ด์ ธ์•ผํ•˜๋Š”๋ฐ

๋ถ„๋ช…ํžˆ ์‚ฌ์ง„์†์—์„œ๋Š” ์•ฝ๊ฐ„ ๊ฐˆ์ƒ‰๋น›์ด ๋Œ์•˜๋˜ ๊ฒƒ ๊ฐ™์•˜๋Š”๋ฐ... ์‹œ๋ป˜๊ฑด ๋ฌด์–ธ๊ฐ€๊ฐ€ ์™”๋‹ค.

์ง„์งœ ์ฒ˜์Œ์—๋Š” ๋‹ค๋ฅธ์‚ฌ๋žŒ ๋ฉ”๋‰ด๊ฐ€ ๋‚˜ํ•œํ…Œ ์ž˜๋ชป ๋ฐฐ๋‹ฌ์˜จ์ค„ ์•Œ์•˜๋‹ค.

๋งค์›Œ ์ฃฝ๋Š”์ค„ ์•Œ์•˜๋Š”๋ฐ ๊ฐ์‚ฌํ•˜๊ฒŒ๋„ ๋ฌผ์„ ๋”ฐ๋ผ์ฃผ์…”์„œ ์‚ด์•˜๋‹ค.(์‚ฌ์‹ค ์ด ๊ธ€์„ ์ ๋Š” ์ง€๊ธˆ๋„ ๋ฐฐ ์•„ํ”ˆ ๊ฒƒ ๊ฐ™๋‹ค)


 

 

 


 

๐Ÿšฒ ํ‡ด๊ทผ! (21:00 -)

2์ฐจ๋กœ ์นดํŽ˜์—์„œ ์•„์ด์Šคํฌ๋ฆผ๊นŒ์ง€ ๋จน๊ณ (๋‹ค์ด์–ดํŠธ ์‹คํŒจใ…‹ใ…‹ใ…‹)

๋‹น์—ฐํžˆ ์ž์ „๊ฑฐ ํƒ€์Šˆํƒ€๊ณ  ํ‡ด๊ทผํ–ˆ๋‹ค. ํ‡ด๊ทผ๊ธธ์— ์ฐ์€ ์‹ ์„ธ๊ณ„ ๋ฐฑํ™”์ ์€ ๋ฉ‹์žˆ๊ตฌ๋งŒ...

์ขŒ)์ฃผํ–‰์ค‘ ์‚ฌ์ง„;;   ์šฐ)์‹ ์„ธ๊ณ„๋ฐฑํ™”์ 


 

๐Ÿ‹๐Ÿป ์šด๋™! (22:00 -)

์—ด์‹ฌํžˆ ๋ฒค์น˜ํ”„๋ ˆ์Šค๋งŒ ํ–ˆ๋‹คใ…Žใ…Ž (ํ•˜์ฒด๋Š” ์ž์ „๊ฑฐ๋กœ ์ด๋ฏธ ์œ ์‚ฐ์†Œ๊ฒธ ํ•˜์ฒด์šด๋™๊ฒธ ๋์ด๋ผ...)

ํ˜„๋Œ€์ธ์€ ์šด๋™๋ถ€์กฑ์ด๋žฌ๋‚˜... ๋‹น์—ฐํžˆ๋„ ๋งค์ผ๊ฐ™์ด ํ—ฌ์Šค์žฅ์— ๊ฐ€์„œ ์šด๋™์„ ํ•˜๋‹ˆ๊นŒ ๋‚œ ์ƒ๊ด€์—†๊ฒ ์ง€?

๊ทผ๋ฐ ํ—ฌ์Šค์žฅ ๋ฐ–์—์„œ ์ด๋ ‡๊ฒŒ ์ฐ์œผ๋‹ˆ ๊ฐ์˜ฅ๊ฐ™....ใ…‹ใ…‹ใ…‹

 

 


 
 
 

๐Ÿ“Œ TODO List:

1. Detection๋ถ„์•ผ Domain Adaptation ๋…ผ๋ฌธ List: 
  โˆ™ Abstract, Conclusion ์ •๋ฆฌ → ppt๋งŒ๋“ค๊ธฐ

2. Deep Learning 2024(Bishop): Chapter 2 ์ฝ๊ณ  ๊ณต๋ถ€ํ•˜๊ธฐ

'2024 winter > ETRI(์ผ์ƒ)' ์นดํ…Œ๊ณ ๋ฆฌ์˜ ๋‹ค๋ฅธ ๊ธ€

[ETRI]2024.01.10  (4) 2024.01.10
[ETRI]2024.01.09  (2) 2024.01.09
[ETRI]2024.01.08  (4) 2024.01.08
[ETRI]2024.01.04  (0) 2024.01.04
[ETRI]2024.01.03  (4) 2024.01.03

+ Recent posts