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

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses arxiv.org 이 논문은 기존의 CNN 모델이 질감, 형태 중 ..
Paper
2024. 3. 25. 15:50