선동이의 삶

목차 Masked-attention Mask Transformer for Universal Image Segmentation CVPR 2022 https://arxiv.org/abs/2112.01527 Masked-attention Mask Transformer for Universal Image Segmentation Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research..

목차 Per-Pixel Classification is Not All You Need for Semantic Segmentation NeurIPS 2021 https://arxiv.org/abs/2107.06278 Per-Pixel Classification is Not All You Need for Semantic Segmentation Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask cla..

목차 iBOT: Image BERT Pre-Training with Online Tokenizer ICLR 2022 https://arxiv.org/abs/2111.07832 iBOT: Image BERT Pre-Training with Online Tokenizer The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces. In this work, we study masked image modeling (MIM) and indicate..

목차 Improved Regularization of Convolutional Neural Networks with Cutout arXiv 2017 https://arxiv.org/abs/1708.04552 Improved Regularization of Convolutional Neural Networks with Cutout Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such represen..

목차 Emerging Properties in Self-Supervised Vision Transformers ICCV 2021 https://arxiv.org/abs/2104.14294 Emerging Properties in Self-Supervised Vision Transformers In 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 arc..

목차 Cut and Learn for Unsupervised Object Detection and Instance Segmentation CVPR 2023 https://arxiv.org/abs/2301.11320 Cut and Learn for Unsupervised Object Detection and Instance Segmentation We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'discover' objects without su..

목차 ObjectStitch: Object Compositing with Diffusion Model CVPR 2023 https://arxiv.org/abs/2212.00932 ObjectStitch: Generative Object Compositing Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training d..

목차 Image Segmentation Using Deep Learning: A Survey https://ieeexplore.ieee.org/abstract/document/9356353 Image Segmentation Using Deep Learning: A Survey Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among o..

목차 Image Segmentation Using Deep Learning: A Survey https://ieeexplore.ieee.org/abstract/document/9356353 Image Segmentation Using Deep Learning: A Survey Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among o..

목차 Image Segmentation Using Deep Learning: A Survey https://ieeexplore.ieee.org/abstract/document/9356353 Image Segmentation Using Deep Learning: A Survey Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among o..