일 | 월 | 화 | 수 | 목 | 금 | 토 |
---|---|---|---|---|---|---|
1 | ||||||
2 | 3 | 4 | 5 | 6 | 7 | 8 |
9 | 10 | 11 | 12 | 13 | 14 | 15 |
16 | 17 | 18 | 19 | 20 | 21 | 22 |
23 | 24 | 25 | 26 | 27 | 28 |
- 파이썬
- Ai
- opencv
- 코딩테스트
- 논문리뷰
- Convolution
- 옵티마이저
- cnn
- 파이토치
- Self-supervised
- optimizer
- programmers
- 인공지능
- 논문 리뷰
- Python
- Semantic Segmentation
- 머신러닝
- 코드구현
- 프로그래머스
- Segmentation
- 논문구현
- ViT
- 딥러닝
- pytorch
- object detection
- transformer
- Paper Review
- 알고리즘
- Computer Vision
- 논문
- Today
- Total
목록논문 리뷰/Multi-Modal (2)
Attention please

이번에 리뷰할 논문은 BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation 입니다. https://paperswithcode.com/paper/blip-bootstrapping-language-image-pre Papers with Code - BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation #3 best model for Open Vocabulary Attribute Detection on OVAD-Box benchmark (mean..

이번에 리뷰할 논문은 Learning Transferable Visual Models From Natural Language Supervision 입니다. https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional label..