Search Results for author:
Found 9 papers, 8 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.
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.
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.
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.
