Recent works on click-based interactive segmentation have demonstrated state-of-the-art results by using various inference-time optimization schemes. These methods are considerably more computationally expensive compared to feedforward approaches, as they require performing backward passes through a network during inference and are hard to deploy on mobile frameworks that usually support only forward passes. In this paper, we extensively evaluate various design choices for interactive segmentation and discover that new state-of-the-art results can be obtained without any additional optimization schemes. Thus, we propose a simple feedforward model for click-based interactive segmentation that employs the segmentation masks from previous steps. It allows not only to segment an entirely new object, but also to start with an external mask and correct it. When analyzing the performance of models trained on different datasets, we observe that the choice of a training dataset greatly impacts the quality of interactive segmentation. We find that the models trained on a combination of COCO and LVIS with diverse and high-quality annotations show performance superior to all existing models. The code and trained models are available at https://github.com/saic-vul/ritm_interactive_segmentation.
Robust Seed Mask Generation for Interactive Image Segmentation
Friedrich-Alexander University Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；Erlangen Graduate School in Advanced Optical Technologies (SAOT)（埃尔朗根先进光学技术研究生院）
Deep learning-based interactive segmentation in remote sensing
National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara（加州大学圣塔芭芭拉分校国家生态分析与综合中心）；University of Idaho（爱达荷大学）；Fujian Agriculture and Forestry University（福建农林大学）；Bren School of Environmental Science and Management, University of California, Santa Barbara（加州大学圣塔芭芭拉分校布伦环境科学与管理学院）
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Shanghai AI Laboratory（上海人工智能实验室）；Sichuan University（四川大学）；Monash University（莫纳什大学）；East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）；Xinjiang University（新疆大学）
MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic Segmentation Network For Relic Landslide Detection
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Institute of Technology（北京理工大学）；China Aero Geophysical Survey and Remote Sensing Center for Natural Resources（中国自然资源航空物探遥感中心）；La Trobe University（拉筹伯大学）
The Hong Kong Polytechnic University（香港理工大学）；ETH Zurich（苏黎世联邦理工学院）；Center for Artificial Intelligence and Robotics, HKISI-CAS（中国科学院香港智能科学研究所人工智能与机器人中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；University of Science and Technology of China（中国科学技术大学）；Faculty of Architecture and the Built Environment, Delft University of Technology（代尔夫特理工大学建筑与环境学院）
QIS : Interactive Segmentation via Quasi-Conformal Mappings
City University of Hong Kong（香港城市大学）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港心脑血管健康工程中心）；Nankai University（南开大学）；Chinese University of Hong Kong（香港中文大学）
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（厦门大学多媒体可信感知与高效计算教育部重点实验室）；Tsinghua University（清华大学）
Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion
Aerospace Center Hospital（航天中心医院）；Yizhun Medical AI Co., Ltd（医准智能医疗科技有限公司）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Southern University of Science and Technology（南方科技大学）；Beijing Institute of Technology（北京理工大学）
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation
Tsinghua Shenzhen International Graduate School（清华深圳国际研究生院）；Tsinghua University（清华大学）；Tencent AI Lab（腾讯人工智能实验室）；University of California, Los Angeles（加州大学洛杉矶分校）；The Chinese University of Hong Kong（香港中文大学）
Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation
School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Sichuan University-Pittsburgh Institute, Sichuan University（四川大学匹兹堡学院）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild
Hunan University（湖南大学）；Manycore Research Institute（码可研究院）；Meituan Group（美团集团）；ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程研究中心）
Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
The Netherlands Cancer Institute（荷兰癌症研究所）；Radboud University Medical Centre（拉德堡德大学医学中心）；Macao Polytechnic University（澳门理工大学）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）
