반응형
250x250
Notice
Recent Posts
Recent Comments
Link
| 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 | 29 | 30 |
Tags
- 에지 검출
- dinov2: learning robust visual features without supervision
- dinov2 논문 리뷰
- evaluating object hallucination in large vision-language models 논문
- dinov2: learning robust visual features without supervision 논문
- blip-2
- vlm hallucination
- 객체 검출
- clip adapter
- dinov2: learning robust visual features without supervision 논문 리뷰
- 딥러닝 엔트로피
- 딥러닝 목적함수
- object detection
- vlm hallucination paper
- evaluating object hallucination in large vision-language models paper
- vlm 환각
- mobilenetv1
- evaluating object hallucination in large vision-language models
- 엔트로피란
- vlm
- polling-based object probing evaluation
- 논문 요약
- 원격 학습 안끊기게
- clip
- Object detection article
- 1차 미분 마스크
- 논문 리뷰
- 기계학습
- 이미지 필터링
- vlm 환각이란
Archives
- Today
- Total
목록딥러닝 clip (1)
My Vision, Computer Vision
[논문 리뷰/요약] CLIP : Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language SupervisionState-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 labeled data is needed to specify any other visual coarxiv.orgAbstract기존 State-of-the-art 컴퓨터 비전 모델은 사전에 정의된, 고정된 객체 범주, Train..
Paper
2025. 1. 20. 12:55