Cermak, G.[Grant]
* 2007: Unsupervised Intrusion Detection Using Color Images
Cermak, J.[Jan]
* 2015: Where Aerosols Become Clouds: Potential for Global Analysis Based on CALIPSO Data
* 2017: Mapping the Twilight Zone: What We Are Missing between Clouds and Aerosols
* 2018: Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10, An
* 2018: Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data
* 2018: Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel
* 2019: Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years
* 2020: New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data, A
* 2022: Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing
* 2024: Mapping Changes in Fractional Vegetation Cover on the Namib Gravel Plains with Satellite-Retrieved Land Surface Emissivity Data
9 for Cermak, J.
Cermak, N.[Nathan]
* 2022: GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging
Cermak, V.[Vojtech]
* 2024: SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification
* 2024: WildlifeDatasets: An open-source toolkit for animal re-identification
Includes: Cermak, V.[Vojtech] Cermák, V.[Vojtech]
Cermakova, I.
* 2016: Using UAV to Detect Shoreline Changes: Case Study: Pohranov Pond, Czech Republic
Includes: Cermakova, I. Cermáková, I.
Cerman, L.[Lukas]
* 2009: Sputnik Tracker: Having a Companion Improves Robustness of the Tracker
* 2012: Tracking with context as a semi-supervised learning and labeling problem
Includes: Cerman, L.[Lukas] Cerman, L.[Lukáš]
Cerman, M.[Martin]
* 2015: LBP and Irregular Graph Pyramids
* 2016: Mobile Recognition System for Analog Energy Meter Scanning, A
* 2016: Topology-based image segmentation using LBP pyramids
Cermelli, F.[Fabio]
* 2019: Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency
* 2020: Modeling the Background for Incremental Learning in Semantic Segmentation
* 2021: Closer Look at Self-training for Zero-Label Semantic Segmentation, A
* 2021: Detecting Anomalies in Semantic Segmentation with Prototypes
* 2022: Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images, A
* 2022: Incremental Learning in Semantic Segmentation from Image Labels
* 2022: Modeling Missing Annotations for Incremental Learning in Object Detection
* 2022: Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
* 2022: Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
* 2022: Relaxing the Forget Constraints in Open World Recognition
* 2023: CoMFormer: Continual Learning in Semantic and Panoptic Segmentation
* 2023: Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model
* 2023: Robust Semantic Segmentation UNCV2023 Challenge Results, The
* 2023: Unmasking Anomalies in Road-Scene Segmentation
14 for Cermelli, F.
Cermeno, E.
* 2013: Learning crowd behavior for event recognition
Cerminara, M.[Matteo]
* 2020: PyTirCam-1.0: A Python Model to Manage Thermal Infrared Camera Data