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Peng MegDet: A Large
Mini-Batch Object Detector
Diba Weakly Supervised
Cascaded Convolutional Networks
Xu Deep Image Matting
Papandreou PersonLab: Person
Pose Estimation and Instance
Segmentation with a Bottom-Up, Part-
Based, Geometric Embedding Model
Zhou Bi-box Regression
for Pedestrian Detection and
Occlusion Estimation
Tripathi Pose2Instance:
Harnessing Keypoints for Person
Instance Segmentation
Papandreou Towards
Accurate Multi-person Pose
Estimation in the Wild
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p
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์Šˆํผ๋ธŒ: L99
์ž‘์—…์ž: L01
https://spb.ai
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์งˆ๋ฌธ์€ Slido์— ๋‚จ๊ฒจ์ฃผ์„ธ์š”.
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TRACK 3
[234]Fast & Accurate Data Annotation Pipeline for AI applications

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[132] ์„œ๋น„์Šค ์˜ค๋ฆฌ์—”ํ‹ฐ๋“œ ๋ธ”๋ก์ฒด์ธ์„ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๋ฌธ์ œ ํ•ด๊ฒฐ[132] ์„œ๋น„์Šค ์˜ค๋ฆฌ์—”ํ‹ฐ๋“œ ๋ธ”๋ก์ฒด์ธ์„ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๋ฌธ์ œ ํ•ด๊ฒฐ
[132] ์„œ๋น„์Šค ์˜ค๋ฆฌ์—”ํ‹ฐ๋“œ ๋ธ”๋ก์ฒด์ธ์„ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๋ฌธ์ œ ํ•ด๊ฒฐNAVER D2
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[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆ
[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆ[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆ
[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆNAVER D2
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ย 
[226]NAVER ๊ด‘๊ณ  deep click prediction: ๋ชจ๋ธ๋ง๋ถ€ํ„ฐ ์„œ๋น™๊นŒ์ง€
[226]NAVER ๊ด‘๊ณ  deep click prediction: ๋ชจ๋ธ๋ง๋ถ€ํ„ฐ ์„œ๋น™๊นŒ์ง€[226]NAVER ๊ด‘๊ณ  deep click prediction: ๋ชจ๋ธ๋ง๋ถ€ํ„ฐ ์„œ๋น™๊นŒ์ง€
[226]NAVER ๊ด‘๊ณ  deep click prediction: ๋ชจ๋ธ๋ง๋ถ€ํ„ฐ ์„œ๋น™๊นŒ์ง€
ย 
[225]NSML: ๋จธ์‹ ๋Ÿฌ๋‹ ํ”Œ๋žซํผ ์„œ๋น„์Šคํ•˜๊ธฐ & ๋ชจ๋ธ ํŠœ๋‹ ์ž๋™ํ™”ํ•˜๊ธฐ
[225]NSML: ๋จธ์‹ ๋Ÿฌ๋‹ ํ”Œ๋žซํผ ์„œ๋น„์Šคํ•˜๊ธฐ & ๋ชจ๋ธ ํŠœ๋‹ ์ž๋™ํ™”ํ•˜๊ธฐ[225]NSML: ๋จธ์‹ ๋Ÿฌ๋‹ ํ”Œ๋žซํผ ์„œ๋น„์Šคํ•˜๊ธฐ & ๋ชจ๋ธ ํŠœ๋‹ ์ž๋™ํ™”ํ•˜๊ธฐ
[225]NSML: ๋จธ์‹ ๋Ÿฌ๋‹ ํ”Œ๋žซํผ ์„œ๋น„์Šคํ•˜๊ธฐ & ๋ชจ๋ธ ํŠœ๋‹ ์ž๋™ํ™”ํ•˜๊ธฐ
ย 
[224]๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰๊ณผ ๊ฐœ์ธํ™”
[224]๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰๊ณผ ๊ฐœ์ธํ™”[224]๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰๊ณผ ๊ฐœ์ธํ™”
[224]๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰๊ณผ ๊ฐœ์ธํ™”
ย 
[216]Search Reliability Engineering (๋ถ€์ œ: ์ง€์ง„์—๋„ ํ”๋“ค๋ฆฌ์ง€ ์•Š๋Š” ๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰์‹œ์Šคํ…œ)
[216]Search Reliability Engineering (๋ถ€์ œ: ์ง€์ง„์—๋„ ํ”๋“ค๋ฆฌ์ง€ ์•Š๋Š” ๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰์‹œ์Šคํ…œ)[216]Search Reliability Engineering (๋ถ€์ œ: ์ง€์ง„์—๋„ ํ”๋“ค๋ฆฌ์ง€ ์•Š๋Š” ๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰์‹œ์Šคํ…œ)
[216]Search Reliability Engineering (๋ถ€์ œ: ์ง€์ง„์—๋„ ํ”๋“ค๋ฆฌ์ง€ ์•Š๋Š” ๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰์‹œ์Šคํ…œ)
ย 
[214] Ai Serving Platform: ํ•˜๋ฃจ ์ˆ˜ ์–ต ๊ฑด์˜ ์ธํผ๋Ÿฐ์Šค๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ๊ณ ๊ตฐ๋ถ„ํˆฌ๊ธฐ
[214] Ai Serving Platform: ํ•˜๋ฃจ ์ˆ˜ ์–ต ๊ฑด์˜ ์ธํผ๋Ÿฐ์Šค๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ๊ณ ๊ตฐ๋ถ„ํˆฌ๊ธฐ[214] Ai Serving Platform: ํ•˜๋ฃจ ์ˆ˜ ์–ต ๊ฑด์˜ ์ธํผ๋Ÿฐ์Šค๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ๊ณ ๊ตฐ๋ถ„ํˆฌ๊ธฐ
[214] Ai Serving Platform: ํ•˜๋ฃจ ์ˆ˜ ์–ต ๊ฑด์˜ ์ธํผ๋Ÿฐ์Šค๋ฅผ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ๊ณ ๊ตฐ๋ถ„ํˆฌ๊ธฐ
ย 
[213] Fashion Visual Search
[213] Fashion Visual Search[213] Fashion Visual Search
[213] Fashion Visual Search
ย 
[232] TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”
[232] TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”[232] TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”
[232] TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”
ย 
[242]์ปดํ“จํ„ฐ ๋น„์ „์„ ์ด์šฉํ•œ ์‹ค๋‚ด ์ง€๋„ ์ž๋™ ์—…๋ฐ์ดํŠธ ๋ฐฉ๋ฒ•: ๋”ฅ๋Ÿฌ๋‹์„ ํ†ตํ•œ POI ๋ณ€ํ™” ํƒ์ง€
[242]์ปดํ“จํ„ฐ ๋น„์ „์„ ์ด์šฉํ•œ ์‹ค๋‚ด ์ง€๋„ ์ž๋™ ์—…๋ฐ์ดํŠธ ๋ฐฉ๋ฒ•: ๋”ฅ๋Ÿฌ๋‹์„ ํ†ตํ•œ POI ๋ณ€ํ™” ํƒ์ง€[242]์ปดํ“จํ„ฐ ๋น„์ „์„ ์ด์šฉํ•œ ์‹ค๋‚ด ์ง€๋„ ์ž๋™ ์—…๋ฐ์ดํŠธ ๋ฐฉ๋ฒ•: ๋”ฅ๋Ÿฌ๋‹์„ ํ†ตํ•œ POI ๋ณ€ํ™” ํƒ์ง€
[242]์ปดํ“จํ„ฐ ๋น„์ „์„ ์ด์šฉํ•œ ์‹ค๋‚ด ์ง€๋„ ์ž๋™ ์—…๋ฐ์ดํŠธ ๋ฐฉ๋ฒ•: ๋”ฅ๋Ÿฌ๋‹์„ ํ†ตํ•œ POI ๋ณ€ํ™” ํƒ์ง€
ย 
[212]C3, ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ์—์„œ ์„œ๋น™๊นŒ์ง€ ๊ฐ€๋Šฅํ•œ ํ•˜๋‘ก ํด๋Ÿฌ์Šคํ„ฐ
[212]C3, ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ์—์„œ ์„œ๋น™๊นŒ์ง€ ๊ฐ€๋Šฅํ•œ ํ•˜๋‘ก ํด๋Ÿฌ์Šคํ„ฐ[212]C3, ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ์—์„œ ์„œ๋น™๊นŒ์ง€ ๊ฐ€๋Šฅํ•œ ํ•˜๋‘ก ํด๋Ÿฌ์Šคํ„ฐ
[212]C3, ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ์—์„œ ์„œ๋น™๊นŒ์ง€ ๊ฐ€๋Šฅํ•œ ํ•˜๋‘ก ํด๋Ÿฌ์Šคํ„ฐ
ย 
[223]๊ธฐ๊ณ„๋…ํ•ด QA: ๊ฒ€์ƒ‰์ธ๊ฐ€, NLP์ธ๊ฐ€?
[223]๊ธฐ๊ณ„๋…ํ•ด QA: ๊ฒ€์ƒ‰์ธ๊ฐ€, NLP์ธ๊ฐ€?[223]๊ธฐ๊ณ„๋…ํ•ด QA: ๊ฒ€์ƒ‰์ธ๊ฐ€, NLP์ธ๊ฐ€?
[223]๊ธฐ๊ณ„๋…ํ•ด QA: ๊ฒ€์ƒ‰์ธ๊ฐ€, NLP์ธ๊ฐ€?
ย 
[231] Clova ํ™”์ž์ธ์‹
[231] Clova ํ™”์ž์ธ์‹[231] Clova ํ™”์ž์ธ์‹
[231] Clova ํ™”์ž์ธ์‹
ย 
[232]TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”
[232]TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”[232]TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”
[232]TensorRT๋ฅผ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ Inference ์ตœ์ ํ™”
ย 
[222]๋ˆ„๊ตฌ๋‚˜ ๋งŒ๋“œ๋Š” ๋‚ด ๋ชฉ์†Œ๋ฆฌ ํ•ฉ์„ฑ๊ธฐ (๋ถ€์ œ: ๊ทธ๊ฒŒ ์ •๋ง ๋˜๋‚˜์š”?)
[222]๋ˆ„๊ตฌ๋‚˜ ๋งŒ๋“œ๋Š” ๋‚ด ๋ชฉ์†Œ๋ฆฌ ํ•ฉ์„ฑ๊ธฐ (๋ถ€์ œ: ๊ทธ๊ฒŒ ์ •๋ง ๋˜๋‚˜์š”?)[222]๋ˆ„๊ตฌ๋‚˜ ๋งŒ๋“œ๋Š” ๋‚ด ๋ชฉ์†Œ๋ฆฌ ํ•ฉ์„ฑ๊ธฐ (๋ถ€์ œ: ๊ทธ๊ฒŒ ์ •๋ง ๋˜๋‚˜์š”?)
[222]๋ˆ„๊ตฌ๋‚˜ ๋งŒ๋“œ๋Š” ๋‚ด ๋ชฉ์†Œ๋ฆฌ ํ•ฉ์„ฑ๊ธฐ (๋ถ€์ œ: ๊ทธ๊ฒŒ ์ •๋ง ๋˜๋‚˜์š”?)
ย 
Old Version: [211] ์ธ๊ณต์ง€๋Šฅ์ด ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์„ ๋งŒ๋“ ๋‹ค
Old Version: [211] ์ธ๊ณต์ง€๋Šฅ์ด ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์„ ๋งŒ๋“ ๋‹คOld Version: [211] ์ธ๊ณต์ง€๋Šฅ์ด ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์„ ๋งŒ๋“ ๋‹ค
Old Version: [211] ์ธ๊ณต์ง€๋Šฅ์ด ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์„ ๋งŒ๋“ ๋‹ค
ย 
[241] AI ์นฉ ๊ฐœ๋ฐœ์— ์‚ฌ์šฉ๋˜๋Š” ์—”์ง€๋‹ˆ์–ด๋ง
[241] AI ์นฉ ๊ฐœ๋ฐœ์— ์‚ฌ์šฉ๋˜๋Š” ์—”์ง€๋‹ˆ์–ด๋ง[241] AI ์นฉ ๊ฐœ๋ฐœ์— ์‚ฌ์šฉ๋˜๋Š” ์—”์ง€๋‹ˆ์–ด๋ง
[241] AI ์นฉ ๊ฐœ๋ฐœ์— ์‚ฌ์šฉ๋˜๋Š” ์—”์ง€๋‹ˆ์–ด๋ง
ย 
[246]QANet: Towards Efficient and Human-Level Reading Comprehension on SQuAD
[246]QANet: Towards Efficient and Human-Level Reading Comprehension on SQuAD[246]QANet: Towards Efficient and Human-Level Reading Comprehension on SQuAD
[246]QANet: Towards Efficient and Human-Level Reading Comprehension on SQuAD
ย 
[221] แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„…แ…ณแ†ฏ แ„‹แ…ตแ„’แ…ขแ„’แ…กแ„‚แ…ณแ†ซ แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„€แ…ฅแ†ทแ„‰แ…ขแ†จ: ํ…์ŠคํŠธ๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€๊ฒ€์ƒ‰์— CNN ์ด์šฉํ•˜๊ธฐ
[221] แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„…แ…ณแ†ฏ แ„‹แ…ตแ„’แ…ขแ„’แ…กแ„‚แ…ณแ†ซ แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„€แ…ฅแ†ทแ„‰แ…ขแ†จ: ํ…์ŠคํŠธ๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€๊ฒ€์ƒ‰์— CNN ์ด์šฉํ•˜๊ธฐ[221] แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„…แ…ณแ†ฏ แ„‹แ…ตแ„’แ…ขแ„’แ…กแ„‚แ…ณแ†ซ แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„€แ…ฅแ†ทแ„‰แ…ขแ†จ: ํ…์ŠคํŠธ๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€๊ฒ€์ƒ‰์— CNN ์ด์šฉํ•˜๊ธฐ
[221] แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„…แ…ณแ†ฏ แ„‹แ…ตแ„’แ…ขแ„’แ…กแ„‚แ…ณแ†ซ แ„‹แ…ตแ„†แ…ตแ„Œแ…ตแ„€แ…ฅแ†ทแ„‰แ…ขแ†จ: ํ…์ŠคํŠธ๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€๊ฒ€์ƒ‰์— CNN ์ด์šฉํ•˜๊ธฐ
ย 
[132] ์„œ๋น„์Šค ์˜ค๋ฆฌ์—”ํ‹ฐ๋“œ ๋ธ”๋ก์ฒด์ธ์„ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๋ฌธ์ œ ํ•ด๊ฒฐ
[132] ์„œ๋น„์Šค ์˜ค๋ฆฌ์—”ํ‹ฐ๋“œ ๋ธ”๋ก์ฒด์ธ์„ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๋ฌธ์ œ ํ•ด๊ฒฐ[132] ์„œ๋น„์Šค ์˜ค๋ฆฌ์—”ํ‹ฐ๋“œ ๋ธ”๋ก์ฒด์ธ์„ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๋ฌธ์ œ ํ•ด๊ฒฐ
[132] ์„œ๋น„์Šค ์˜ค๋ฆฌ์—”ํ‹ฐ๋“œ ๋ธ”๋ก์ฒด์ธ์„ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๋ฌธ์ œ ํ•ด๊ฒฐ
ย 
[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆ
[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆ[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆ
[145] printf("Hello, AR"); //์„ธ์ƒ์„ ๋ฐ”๊พธ๋Š” ์ƒˆ๋กœ์šด ๋ˆˆ
ย 

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