We introduce a new, challenging benchmark and a dataset, FungiTastic, based on fungal records continuously collected over a twenty-year span. The dataset is labelled and curated by experts and consists of about 350k multimodal observations of 6k fine-grained categories (species). The fungi observations include photographs and additional data, e.g., meteorological and climatic data, satellite images, and body part segmentation masks. FungiTastic is one of the few benchmarks that include a test set with DNA-sequenced ground truth of unprecedented label reliability. The benchmark is designed to support (i) standard closed-set classification, (ii) open-set classification, (iii) multi-modal classification, (iv) few-shot learning, (v) domain shift, and many more. We provide tailored baselines for many use cases, a multitude of ready-to-use pre-trained models on https://huggingface.co/collections/BVRA/fungitastic-66a227ce0520be533dc6403b, and a framework for model training. The documentation and the baselines are available at https://github.com/BohemianVRA/FungiTastic/ and https://www.kaggle.com/datasets/picekl/fungitastic.
Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd.（远景智能有限公司）；Peking Union Medical College Hospital（北京协和医院）；Chinese Academy of Medical Sciences（中国医学科学院）
Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd.（远见智能有限公司）；Chinese Academy of Medical Sciences（中国医学科学院）；Peking Union Medical College Hospital（北京协和医院）
Multi-modal panoramic 3D outdoor datasets for place categorization
Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程研究生院）；Polytechnic University of Cartagena (UPCT)（卡塔赫纳理工学院）；Faculty of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程学系）
CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound
Hangzhou Dianzi University（杭州电子科技大学）；the Second Affiliated Hospital of Zhejiang University（浙江大学医学院附属第二医院）；Waseda University（早稻田大学）；Communication University of Zhejiang（浙江传媒学院）
Learning Geometric and Photometric Features from Panoramic LiDAR Scans for Outdoor Place Categorization
Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程研究生院）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Faculty of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电子工程学部）；Technical University of Cartagena（卡塔赫纳技术大学）
AWM-Fuse: Multi-Modality Image Fusion for Adverse Weather via Global and Local Text Perception
Guangdong-HongKong-Macao Joint Laboratory for Intelligent MicroNano Optoelectronic Technology（粤港澳联合智能微纳光电子技术联合实验室）；Foshan University（佛山大学）；School of Mechanical Electronic and Information Engineering（机械电子信息工程学院）；China University of Mining and Technology（中国矿业大学）；School of Information Engineering and Automation（信息工程与自动化学院）
Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；School of Computer Science, University of Adelaide（阿德莱德大学计算机科学学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）
Research on Image Recognition Technology Based on Multimodal Deep Learning
Stony Brook University（石溪大学）；University of Southern California（南加州大学）；New York University（纽约大学）；Northeastern University（东北大学）；Columbia University（哥伦比亚大学）
