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
- mobilenetv1
- 에지 검출
- 이미지 필터링
- 딥러닝 목적함수
- dinov2: learning robust visual features without supervision 논문 리뷰
- evaluating object hallucination in large vision-language models
- evaluating object hallucination in large vision-language models paper
- evaluating object hallucination in large vision-language models 논문
- vlm 환각이란
- blip-2
- 논문 요약
- dinov2: learning robust visual features without supervision 논문
- Object detection article
- vlm hallucination paper
- clip adapter
- 딥러닝 엔트로피
- 논문 리뷰
- vlm hallucination
- 원격 학습 안끊기게
- 엔트로피란
- clip
- dinov2: learning robust visual features without supervision
- 객체 검출
- vlm
- dinov2 논문 리뷰
- 기계학습
- vlm 환각
- polling-based object probing evaluation
- 1차 미분 마스크
- object detection
Archives
- Today
- Total
목록clip-adapter paper (1)
My Vision, Computer Vision

CLIP-Adapter: Better Vision-Language Models with Feature AdaptersLarge-scale contrastive vision-language pre-training has shown significant progress in visual representation learning. Unlike traditional visual systems trained by a fixed set of discrete labels, a new paradigm was introduced in \cite{radford2021learning}arxiv.orgProblem(문제 지적)CLIP은 클래스 이름을 프롬프트 템플릿에 넣어 Zero-shot transfer를 수행하는데, 이..
Paper
2025. 2. 5. 13:54