| 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 |
- 엔트로피란
- evaluating object hallucination in large vision-language models paper
- 에지 검출
- 이미지 필터링
- 기계학습
- 딥러닝 목적함수
- 논문 요약
- object detection
- 딥러닝 엔트로피
- 논문 리뷰
- vlm hallucination paper
- dinov2 논문 리뷰
- polling-based object probing evaluation
- dinov2: learning robust visual features without supervision 논문 리뷰
- evaluating object hallucination in large vision-language models 논문
- vlm
- dinov2: learning robust visual features without supervision
- clip adapter
- vlm 환각이란
- vlm hallucination
- clip
- 원격 학습 안끊기게
- Object detection article
- blip-2
- evaluating object hallucination in large vision-language models
- 객체 검출
- dinov2: learning robust visual features without supervision 논문
- mobilenetv1
- vlm 환각
- 1차 미분 마스크
- Today
- Total
목록Paper (41)
My Vision, Computer Vision
[논문 요약/리뷰] LLaMA: Open and Efficient Foundation Language Models
LLaMA: Open and Efficient Foundation Language ModelsWe introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, withoarxiv.org발행일 : 2023. 02. 27.Meta AIProblem최근, 한정된 컴퓨터 예산(Budget)에서 LLM과 데이터셋 크기에 대한 최적화 연구..
[논문 리뷰/요약] BEIT: BERT Pre-Training of Image Transformers
BEiT: BERT Pre-Training of Image TransformersWe introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to prearxiv.orgProblemBEIT(Bidirectional Encoder representation from Image Transformers)를 제안한다.ViT는 CNN..
[논문 리뷰/요약] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingWe introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlaarxiv.org발행일 : 2018. 10. 11.Google AI LanguageProblem기존의 Langu..
[논문 리뷰/요약] BLIP-2: Bootstrapping Language-Image Pre-training
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsThe cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from oarxiv.org Problem대부분의 State-of-the-art V..
[논문 리뷰/요약] CLIPPO: Image-and-Language Understanding from Pixels Only
CLIPPO: Image-and-Language Understanding from Pixels OnlyMultimodal models are becoming increasingly effective, in part due to unified components, such as the Transformer architecture. However, multimodal models still often consist of many task- and modality-specific pieces and training procedures. For example,arxiv.orgProblem대부분의 멀티모달 모델은 모달리티 별로 구성 요소가 다르고, 다른 태스크에 적용하기 위해 그에 맞는 추가적인 학습 절차가 필요..
[논문 리뷰/요약] CLIP-Adapter: Better Vision-Language Models with Feature Adapters
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..