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Found 8 papers, 7 papers with code
Benchmarking Representations for Speech, Music, and Acoustic Events
Limited diversity in standardized benchmarks for evaluating audio representation learning (ARL) methods may hinder systematic comparison of current methods' capabilities.
QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1
Identification and analysis of all affected areas is mandatory to support areas not monitored by traditional stations.
M3-VRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding
This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding.
ViGEO: an Assessment of Vision GNNs in Earth Observation
Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings.
CaBuAr: California Burned Areas dataset for delineation
Forest wildfires represent one of the catastrophic events that, over the last decades, caused huge environmental and humanitarian damages.
DQNC2S: DQN-based Cross-stream Crisis event Summarizer
Summarizing multiple disaster-relevant data streams simultaneously is particularly challenging as existing Retrieve&Re-ranking strategies suffer from the inherent redundancy of multi-stream data and limited scalability in a multi-query setting.
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework.
Scaling associative classification for very large datasets
DAC exploits ensemble learning to distribute the training of an associative classifier among parallel workers and improve the final quality of the model.
