Prosthetic vision is being applied to partially recover the retinal stimulation of visually impaired people. However, the phosphenic images produced by the implants have very limited information bandwidth due to the poor resolution and lack of color or contrast. The ability of object recognition and scene understanding in real environments is severely restricted for prosthetic users. Computer vision can play a key role to overcome the limitations and to optimize the visual information in the simulated prosthetic vision, improving the amount of information that is presented. We present a new approach to build a schematic representation of indoor environments for phosphene images. The proposed method combines a variety of convolutional neural networks for extracting and conveying relevant information about the scene such as structural informative edges of the environment and silhouettes of segmented objects. Experiments were conducted with normal sighted subjects with a Simulated Prosthetic Vision system. The results show good accuracy for object recognition and room identification tasks for indoor scenes using the proposed approach, compared to other image processing methods.
Simulation of prosthetic vision with PRIMA system and enhancement of face representation
Department of Electrical Engineering, Stanford University（电气工程系，斯坦福大学）；Hansen Experimental Physics Laboratory, Stanford University（哈恩实验物理实验室，斯坦福大学）；Department of Ophthalmology, Stanford University（眼科学系，斯坦福大学）
Boundary-Aware Vision Transformer for Angiography Vascular Network Segmentation
College of Computing and Informatics, University of Sharjah（计算机与信息学院，沙迦大学）；Scientific and Technical Research Center for the Development of Arabic Language (CRSTDLA)（阿拉伯语言发展科学技术研究中心（CRSTDLA））；Artificial Intelligence Research Institute（人工智能研究所）；Skolkovo Institute of Science and Technology（斯克尔科沃科学与技术研究所）；Bauman Moscow State Technical University（莫斯科巴乌姆技术大学）；Moscow Technical University of Communications and Informatics（莫斯科通信与信息大学）；BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory（BIMAI实验室，生物医学引导的人工智能实验室）
EAVL: Explicitly Align Vision and Language for Referring Image Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Science（中国科学院大学人工智能学院）
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）
LViT: Language meets Vision Transformer in Medical Image Segmentation
Xiamen University（厦门大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Hull（赫尔大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation
South China University of Technology（华南理工大学）；Pengcheng Laboratory（鹏城实验室）；China Tobacco Guangdong Industrial Corporation（广东中烟工业有限责任公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Meituan Inc.（美团公司）；University of Trento（特伦托大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）
Fine-tuning vision foundation model for crack segmentation in civil infrastructures
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Civil Engineering, Tsinghua University（清华大学土木工程系）；School of Civil and Resource Engineering, University of Science and Technology Beijing（北京科技大学土木与资源工程学院）
CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（J. Crayton Pruitt家庭生物医学工程系，佛罗里达大学）；Department of Medicine, University of Florida（医学系，佛罗里达大学）
SymbolSight: Minimizing Inter-Symbol Interference for Reading with Prosthetic Vision
Department of Computer Science, University of California, Santa Barbara（计算机科学系，加州大学圣芭芭拉分校）；Department of Psychological & Brain Sciences, University of California, Santa Barbara（心理学与脑科学系，加州大学圣芭芭拉分校）
Decomposed Vision-Language Alignment for Fine-Grained Open-Vocabulary Segmentation
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang University（浙江大学）
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
University of Oxford（牛津大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学清华-伯克利深圳学院）；SenseTime Research（商汤科技研究院）；The University of Hong Kong（香港大学）
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Cambridge University（剑桥大学）；Beijing Shijitan Hospital, Capital Medical University（首都医科大学附属北京世纪坛医院）；University of Liverpool（利物浦大学）
