Deep neural networks trained to inpaint partially occluded images show a deep understanding of image composition and have even been shown to remove objects from images convincingly. In this work, we investigate how this implicit knowledge of image composition can be leveraged for fully self-supervised instance separation. We propose a measure for the independence of two image regions given a fully self-supervised inpainting network and separate objects by maximizing this independence. We evaluate our method on two microscopy image datasets and show that it reaches similar segmentation performance to fully supervised methods.
Qatar University（卡塔尔大学）；University of Warwick（华威大学）；Kyungpook National University（庆北国立大学）
In many advanced video based applications background modeling is a pre-processing step to eliminate redundant data, for instance in tracking or video surveillance applications. Over the past years background subtraction is usually based on low level or hand-crafted features such as raw color components, gradients, or local binary patterns. The background subtraction algorithms performance suffer in the presence of various challenges such as dynamic backgrounds, photometric variations, camera jitters, and shadows. To handle these challenges for the purpose of accurate background modeling we pro…
InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior
University of Science and Technology of China（中国科学技术大学）；Alibaba Group（阿里巴巴集团）；The Hong Kong University of Science and Technology（香港科技大学）；Ant Group（蚂蚁集团）
Separating an image into cartoon and texture components comes useful in image processing applications, such as image compression, image segmentation, image inpainting. Yves Meyer's influential cartoon texture decomposition model involves deriving an energy functional by choosing appropriate spaces and functionals. Minimizers of the derived energy functional are cartoon and texture components of an image. In this study, cartoon part of an image is separated, by reconstructing it from pixels of multi scale Total-Variation filtered versions of the original image which is sought to be decomposed i…
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）
Image Inpainting is a task that aims to fill in missing regions of corrupted images with plausible contents. Recent inpainting methods have introduced perceptual and style losses as auxiliary losses to guide the learning of inpainting generators. Perceptual and style losses help improve the perceptual quality of inpainted results by supervising deep features of generated regions. However, two challenges have emerged with the usage of auxiliary losses: (i) the time-consuming grid search is required to decide weights for perceptual and style losses to properly perform, and (ii) loss terms with d…
Samsung AI Centre Toronto（多伦多三星人工智能中心）；University of Toronto（多伦多大学）；York University（约克大学）；Vector Institute for AI（向量人工智能研究所）
Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the replaced region is visually plausible and consistent with its context. We refer to this task as 3D inpainting. In 3D, solutions must be both consistent across multiple views and geometrically valid. In this paper, we propose a novel 3D inpainting method that addresses these challenge…
Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator
Data Science and AI Laboratory, ECE, Seoul National University（数据科学与人工智能实验室、电子与计算机工程系、首尔国立大学）；AIIS, ASRI, INMC, ISRC, and Interdisciplinary Program in AI, Seoul National University（人工智能研究所、人工智能研究室、智能网络与计算中心、信息科学与工程系以及人工智能交叉学科项目、首尔国立大学）
Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
Aeronautics Institute of Technology（航空技术学院）；Science and Technology Institute, Federal University of São Paulo（圣保罗联邦大学科学与技术研究所）；LASIGE, Faculdade de Ciências, Universidade de Lisboa（里斯本大学理学院LASIGE实验室）；University of Coimbra, CISUC/LASI – Centre for Informatics and Systems of the University of Coimbra（科英布拉大学CISUC/LASI - 科英布拉大学信息与系统中心）；Instituto Superior Técnico, Universidade de Lisboa, Instituto de Telecomunicações（里斯本大学高等技术学院、电信研究所）
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Center for Applied Scientific Computing（应用科学计算中心）
Solving inverse problems continues to be a challenge in a wide array of applications ranging from deblurring, image inpainting, source separation etc. Most existing techniques solve such inverse problems by either explicitly or implicitly finding the inverse of the model. The former class of techniques require explicit knowledge of the measurement process which can be unrealistic, and rely on strong analytical regularizers to constrain the solution space, which often do not generalize well. The latter approaches have had remarkable success in part due to deep learning, but require a large coll…
