The objective of this paper is a model that is able to discover, track and segment multiple moving objects in a video. We make four contributions: First, we introduce an object-centric segmentation model with a depth-ordered layer representation. This is implemented using a variant of the transformer architecture that ingests optical flow, where each query vector specifies an object and its layer for the entire video. The model can effectively discover multiple moving objects and handle mutual occlusions; Second, we introduce a scalable pipeline for generating multi-object synthetic training data via layer compositions, that is used to train the proposed model, significantly reducing the requirements for labour-intensive annotations, and supporting Sim2Real generalisation; Third, we conduct thorough ablation studies, showing that the model is able to learn object permanence and temporal shape consistency, and is able to predict amodal segmentation masks; Fourth, we evaluate our model, trained only on synthetic data, on standard video segmentation benchmarks, DAVIS, MoCA, SegTrack, FBMS-59, and achieve state-of-the-art performance among existing methods that do not rely on any manual annotations. With test-time adaptation, we observe further performance boosts.
Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images
Medical University of Vienna（维也纳医科大学）；Carl Zeiss Meditec, Inc.（卡尔蔡司医疗技术公司）
视网膜成像数据中解剖特征的自动检测和定位与许多方面相关。在这项工作中，我们遵循以数据为中心的方法来优化分类器训练，用于在视网膜的光学相干断层扫描正面图像中进行视神经乳头检测和定位。我们研究领域知识驱动的空间复杂性降低对所产生的视神经乳头分割和定位性能的影响。我们提出了一种机器学习方法，用于在3D宽场扫频源光学相干断层扫描的2D正面投影中分割视神经乳头，该方法能够自动评估大量数据。对视网膜的手动注释的2D en face图像的评估表明，当底层像素级二进制分类任务通过领域知识在空间上放松时，标准U-Net的训练可以产生改进的视神经乳头分割和定位性能。
ORGAN: Object-Centric Representation Learning using Cycle Consistent Generative Adversarial Networks
Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, University and ETH Zurich（生物传感器与生物电子实验室，生物医学工程研究所，大学和ETH苏黎世分校）；Department of Neurology, Insel Gruppe, Bern, Switzerland（神经病学系，因塞格鲁普，瑞士伯恩）；University of California, San Francisco, USA（加州大学旧金山分校，美国）；Department of Neurobiology, University of Chicago, 951 E 58th St, Chicago, 60637, IL, USA（神经生物学系，芝加哥大学，951 E 58th St, 奇克阿哥，60637, IL, 美国）；Department of Physics, University of Chicago, 929 E 57th St, Chicago, 60637, IL, USA（物理学系，芝加哥大学，929 E 57th St, 奇克阿哥，60637, IL, 美国）
COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts
Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences（中国科学院福建物质结构研究所）；Institute of Urban Environment, Chinese Academy of Sciences（中国科学院城市环境研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation
Samsung Research China - Beijing (SRC-B)（三星中国研究院（北京））；Samsung Advanced Institute of Technology (SAIT)（三星先进技术研究院）；University of Oxford（牛津大学）；The University of Hong Kong（香港大学）
NeoWorld: Neural Simulation of Explorable Virtual Worlds via Progressive 3D Unfolding
MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能联合实验室、人工智能研究院、上海交通大学）；vivo Mobile Communication Co., Ltd.（vivo移动通信有限公司）
