전체 글19 SAM 논문 리뷰 https://arxiv.org/abs/2304.02643 Segment AnythingWe introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensearxiv.org 1. Introduction웹 규모 데이터셋으로 사전 훈련된 대규모 언어 모델은 강력한 zero-shot 및 few-shot 일반화로 자연어 처리(.. 2025. 8. 12. Stable Diffusion 논문 리뷰 https://arxiv.org/abs/2112.10752 High-Resolution Image Synthesis with Latent Diffusion ModelsBy decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism tarxiv.org1. Introduction이미지 합성은 컴퓨터 비전에서 많은 발전과 수.. 2025. 8. 5. DINO 논문 리뷰 https://arxiv.org/abs/2104.14294 Emerging Properties in Self-Supervised Vision TransformersIn this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particarxiv.org1. IntroductionTransformer는 비전에서 CNN의 대안으로.. 2025. 7. 29. DETR 논문 리뷰 https://arxiv.org/abs/2005.12872 End-to-End Object Detection with TransformersWe present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor genearxiv.org1 IntroductionObject Detection의 목표는 바운딩 박스와 카테고리의 집합{(C.. 2025. 7. 22. 이전 1 2 3 4 5 다음
