Dynamic Feature Fusion for Semantic Edge Detection
Institute of Remote Sensing and Digital Earth, CAS（中国科学院遥感与数字地球研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）
Features from multiple scales can greatly benefit the semantic edge detection task if they are well fused. However, the prevalent semantic edge detection methods apply a fixed weight fusion strategy where images with different semantics are forced to share the same weights, resulting in universal fusion weights for all images and locations regardless of their different semantics or local context. In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively. This is achieved by a proposed weight learner to infer proper fusion weights over multi-level features for each location of the feature map, conditioned on the specific input. In this way, the heterogeneity in contributions made by different locations of feature maps and input images can be better considered and thus help produce more accurate and sharper edge predictions. We show that our model with the novel dynamic feature fusion is superior to fixed weight fusion and also the naïve location-invariant weight fusion methods, via comprehensive experiments on benchmarks Cityscapes and SBD. In particular, our method outperforms all existing well established methods and achieves new state-of-the-art.
SIESEF-FusionNet: Spatial Inter-correlation Enhancement and Spatially-Embedded Feature Fusion Network for LiDAR Point Cloud Semantic Segmentation
Intelligent Autonomous Systems Laboratory, Shanghai University of Electric Power（上海电力大学智能自主系统实验室）；College of Literature, Science and the Arts, University of Michigan-Ann Arbor（密歇根大学安娜堡分校文学、科学与艺术学院）
3D Gated Recurrent Fusion for Semantic Scene Completion
The University of Adelaide（阿德莱德大学）；Nanjing University of Science and Technology（南京理工大学）；Northwestern Polytechnical University（西北工业大学）；ETH Zurich（苏黎世联邦理工学院）
AD-SRF: Attack-Defense Semantic Reinforcement for Multi-Modal Image Fusion
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Robotics, Wuhan University（武汉大学机器人学院）；Suzhou Institute, Wuhan University（武汉大学苏州研究院）
MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室，吉林大学）；Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；National Engineering Research Center of Visual Technology, School of Computer Science, Peking University（视觉技术国家工程研究中心，北京大学计算机科学学院）
Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation
Air Force Engineering University（空军工程大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Sydney（悉尼大学）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）
SMFusion: Semantic-Preserving Fusion of Multimodal Medical Images for Enhanced Clinical Diagnosis
College of Information and Intelligence, Hunan Agricultural University（湖南农业大学信息与智能学院）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）
SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion
School of Software Engineering, Beijing Jiaotong University（北京交通大学软件工程学院）；School of Astronautics, Beihang University（北航航天学院）；Faculty of Engineering and Information Technology, University of Melbourne（墨尔本大学工程与信息技术学院）；Ordos Laboratory, Ordos（鄂尔多斯实验室）
U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation
Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））
Robust Semantic Segmentation By Dense Fusion Network On Blurred VHR Remote Sensing Images
Shenzhen Institutes of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；Beijing Futong Dongfang Technology Co, Ltd（北京富通东方科技有限公司）；Beijing University of Chemical Technology（北京化工大学）
MS-Occ: Multi-Stage LiDAR-Camera Fusion for 3D Semantic Occupancy Prediction
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology（光学电子与计算机工程学院，上海科学技术大学）；School of Automotive Studies, Tongji University（汽车学院，同济大学）；Momoni AI；College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；School of Engineering, Swinburne University of Technology（工程学院，斯威本技术大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电气与电子工程学院，南洋理工大学）
FS-Diff: Semantic guidance and clarity-aware simultaneous multimodal image fusion and super-resolution
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Physics and Optoelectronic Engineering, Foshan University（佛山大学物理与光电工程学院）；School of Instrumentation Science and Optelectronics Engineering, Beihang University（北航仪器科学与光电工程学院）；School of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）
VCS-SLAM: Visibility-Consistent Semantic Fusion for 3D Gaussian SLAM
New York University, Electrical & Computer Engineering Dept.（纽约大学电气与计算机工程系）；Embodied AI and Robotics (AIR) Lab, NYU Abu Dhabi（纽约大学阿布扎比分校 embodied AI and Robotics (AIR) 实验室）；New York University Abu Dhabi, Electrical Engineering（纽约大学阿布扎比分校 电气工程）
