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Found 26 papers, 13 papers with code
Leveraging Self-Supervised Learning for Scene Recognition in Child Sexual Abuse Imagery
In light of that, reliable automated tools that can securely and efficiently deal with this data are paramount.
Data-Centric Machine Learning for Geospatial Remote Sensing Data
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
YOLOv7 for Mosquito Breeding Grounds Detection and Tracking
With the looming threat of climate change, neglected tropical diseases such as dengue, zika, and chikungunya have the potential to become an even greater global concern.
FuSS: Fusing Superpixels for Improved Segmentation Consistency
In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation.
Seeing without Looking: Analysis Pipeline for Child Sexual Abuse Datasets
The online sharing and viewing of Child Sexual Abuse Material (CSAM) are growing fast, such that human experts can no longer handle the manual inspection.
Conditional Reconstruction for Open-set Semantic Segmentation
Open set segmentation is a relatively new and unexploredtask, with just a handful of methods proposed to model suchtasks. We propose a novel method called CoReSeg thattackles the issue using class conditional reconstruction ofthe input images according to their pixelwise mask.
Weakly Supervised Few-Shot Segmentation Via Meta-Learning
Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis.
Learning to Segment Medical Images from Few-Shot Sparse Labels
In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images.
Few-Shot Semantic Segmentation
Medical Image Segmentation
+2
Opening Deep Neural Networks with Generative Models
Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes.
A New Similarity Space Tailored for Supervised Deep Metric Learning
We propose a novel deep metric learning method.
A Soft Computing Approach for Selecting and Combining Spectral Bands
We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks.
AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification
Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery.
Fully Convolutional Open Set Segmentation
In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches.
BrazilDAM: A Benchmark dataset for Tailings Dam Detection
In the experiments, we achieved an average classification accuracy of 94. 11% in tailing dam binary classification task.
Towards Open-Set Semantic Segmentation of Aerial Images
A more uncommon source of images exploited in the remote sensing field are satellite and aerial images.
SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition
Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community.
Ranked #16 on
Action Recognition
on NTU RGB+D 120
An Introduction to Deep Morphological Networks
Results show that the proposed DeepMorphNets is a promising technique that can learn distinct features when compared to the ones learned by current deep learning methods.
Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks
Plant phenology studies rely on long-term monitoring of life cycles of plants.
Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation
We merge these unsupervised networks with supervised deep semantic segmentation architectures in order to create a semi-supervised method capable of learning from both unlabeled and labeled data, whenever labeling is available.
A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping
In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns.
Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties.
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization
Recently, data-driven feature learning approaches have been successfully explored as alternatives for producing more representative visual features.
Exploiting ConvNet Diversity for Flooding Identification
Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities.
Activity Recognition based on a Magnitude-Orientation Stream Network
The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information.
Meat adulteration detection through digital image analysis of histological cuts using LBP
Food fraud has been an area of great concern due to its risk to public health, reduction of food quality or nutritional value and for its economic consequences.
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors.
