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Found 6 papers, 1 papers with code
Semi Supervised Semantic Segmentation Using Generative Adversarial Network
Semantic segmentation has been a long standing challenging task in computer vision.
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
Semantic segmentation has been a long standing challenging task in computer vision.
Deep Learning Human Mind for Automated Visual Classification
In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.
Scene Labeling Through Knowledge-Based Rules Employing Constrained Integer Linear Programing
In this paper, we propose to use high-level knowledge regarding rules in the inference to incorporate dependencies among regions in the image to improve scores of classification.
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks
Using an over-complete dictionary of the covariance based descriptors built from labeled training samples, we formulate low-level event recognition as a sparse linear approximation problem.
Scene Labeling Using Sparse Precision Matrix
To do this, we formulate the problem as an energy minimization over a graph, whose structure is captured by applying sparse constraint on the elements of the precision matrix.
