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Found 10 papers, 6 papers with code
Weed mapping in multispectral drone imagery using lightweight vision transformers
The method uses a lightweight Transformer architecture to process high-resolution aerial images obtained from drones and performs semantic segmentation to distinguish between crops and weeds.
Applying Knowledge Distillation to Improve Weed Mapping With Drones
In precision agriculture, non-invasive remote sensing using UAVs can be employed to observe crops in visible and non-visible spectra.
Exploring the Synergy Between Vision-Language Pretraining and ChatGPT for Artwork Captioning: A Preliminary Study
While AI techniques have enabled automated analysis and interpretation of visual content, generating meaningful captions for artworks presents unique challenges.
Density-based clustering with fully-convolutional networks for crowd flow detection from drones
To this end, we propose a crowd flow detection method for video sequences shot by a drone.
Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PET
Dementia is one of the most common diseases in the elderly and a leading cause of mortality and disability.
Ranked #1 on
Medical Image Classification
on OASIS 3
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
A deep learning approach to clustering visual arts
The method uses a pre-trained convolutional network to extract features and then feeds these features into a deep embedded clustering model, where the task of mapping the input data to a latent space is jointly optimized with the task of finding a set of cluster centroids in this latent space.
Integrating Contextual Knowledge to Visual Features for Fine Art Classification
Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community.
Deep convolutional embedding for digitized painting clustering
The proposed method can be useful for several art-related tasks, in particular visual link retrieval and historical knowledge discovery in painting datasets.
Visual link retrieval and knowledge discovery in painting datasets
Visual arts are of inestimable importance for the cultural, historic and economic growth of our society.
