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Found 5 papers, 2 papers with code
GroundingGPT:Language Enhanced Multi-modal Grounding Model
Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input.
SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction
Training specific deep learning models for particular tasks is common across various domains within seismology.
Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)
Overall, our study provides for the first time a prototype self-consistent all-in-one system of simultaneous seismic phase picking, association, and location, which has the potential for next-generation autonomous earthquake monitoring.
Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan earthquake
In this paper, we present a CNN-based Phase- Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets.
Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks
Recently, semantic segmentation and general object detection frameworks have been widely adopted by scene text detecting tasks.
