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Found 12 papers, 3 papers with code
Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging
Glaucoma, a leading cause of irreversible blindness, necessitates early detection for accurate and timely intervention to prevent irreversible vision loss.
BEE-NET: A deep neural network to identify in-the-wild Bodily Expression of Emotions
In this study, we investigate how environmental factors, specifically the scenes and objects involved, can affect the expression of emotions through body language.
Deep Learning and Computer Vision for Glaucoma Detection: A Review
Glaucoma is the leading cause of irreversible blindness worldwide and poses significant diagnostic challenges due to its reliance on subjective evaluation.
Explanation Shift: How Did the Distribution Shift Impact the Model?
Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions.
ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks
To this end, we propose using adaptive bandwidth kernel density estimation to assign a relevance score to each unit of the feature map with respect to the predicted class.
A deep convolutional neural network for classification of Aedes albopictus mosquitoes
Monitoring the spread of disease-carrying mosquitoes is a first and necessary step to control severe diseases such as dengue, chikungunya, Zika or yellow fever.
On the use of uncertainty in classifying Aedes Albopictus mosquitoes
The estimated uncertainty was also used in an active learning framework, where just a portion of the data from large training sets was manually labelled.
Explainable, automated urban interventions to improve pedestrian and vehicle safety
At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e. g. the problems of congestion and pollution.
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures.
VICSOM: VIsual Clues from SOcial Media for psychological assessment
The contribution is two-fold: (i) we provide a large multimodal database from Instagram public profiles (more than 30, 000 images and text captions) annotated by expert Psychologists on each perceived behavior according to Glasser's theory, and (ii) we propose to automate the recognition of the (unconsciously) perceived needs by the users.
Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition
Automated emotion recognition in the wild from facial images remains a challenging problem.
Speeding Up Neural Networks for Large Scale Classification using WTA Hashing
In this paper we propose to use the Winner Takes All hashing technique to speed up forward propagation and backward propagation in fully connected layers in convolutional neural networks.
