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Found 15 papers, 13 papers with code
Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation
In particular, the local and global encoders extract information from the same input at different granularities.
Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications
The Real-time Transport Protocol (RTP)-based real-time communications (RTC) applications, exemplified by video conferencing, have experienced an unparalleled surge in popularity and development in recent years.
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
Large Language Models (LLMs) have demonstrated impressive performance across various tasks.
Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment
Our approach leverages VLMs to analyze frames from video game tutorials, answer relevant questions to simulate human perception, and provide feedback.
KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
This paper analyzes the integration of KAN layers into the U-Net architecture (U-KAN) to segment crop fields using Sentinel-2 and Sentinel-1 satellite images and provides an analysis of the performance and explainability of these networks.
Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation
Our findings suggest that our proposed Depth Any Canopy, the result of fine-tuning the Depth Anything v2 model for canopy height estimation, provides a performant and efficient solution, surpassing the current state-of-the-art with superior or comparable performance using only a fraction of the computational resources and parameters.
Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking
Earthquakes are commonly estimated using physical seismic stations, however, due to the installation requirements and costs of these stations, global coverage quickly becomes impractical.
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.
3MVRD: 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.
