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Found 8 papers, 3 papers with code
Edge Detection for Satellite Images without Deep Networks
Satellite imagery is widely used in many application sectors, including agriculture, navigation, and urban planning.
An Empirical Method to Quantify the Peripheral Performance Degradation in Deep Networks
When applying a convolutional kernel to an image, if the output is to remain the same size as the input then some form of padding is required around the image boundary, meaning that for each layer of convolution in a convolutional neural network (CNN), a strip of pixels equal to the half-width of the kernel size is produced with a non-veridical representation.
Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations
Furthermore, we investigate the effect of training state-of-the-art CNN-based saliency models on these types of stimuli and conclude that the additional training data does not lead to a significant improvement of their ability to find odd-one-out targets.
Rapid Visual Categorization is not Guided by Early Salience-Based Selection
The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements.
SMILER: Saliency Model Implementation Library for Experimental Research
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models.
Active Fixation Control to Predict Saccade Sequences
However, on static images the emphasis of these models has largely been based on non-ordered prediction of fixations through a saliency map.
Saccade Sequence Prediction: Beyond Static Saliency Maps
The accuracy of such models has dramatically increased recently due to deep learning.
Spatially Binned ROC: A Comprehensive Saliency Metric
A recent trend in saliency algorithm development is large-scale benchmarking and algorithm ranking with ground truth provided by datasets of human fixations.
