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Found 6 papers, 0 papers with code
Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks
We study the role of the context on interfering with a disentangled foreground target object representation in this work.
Compact Neural Representation Using Attentive Network Pruning
Network parameter reduction methods have been introduced to systematically deal with the computational and memory complexity of deep networks.
Selective Segmentation Networks Using Top-Down Attention
Convolutional neural networks model the transformation of the input sensory data at the bottom of a network hierarchy to the semantic information at the top of the visual hierarchy.
STNet: Selective Tuning of Convolutional Networks for Object Localization
Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks.
Active Control of Camera Parameters for Object Detection Algorithms
Camera parameters not only play an important role in determining the visual quality of perceived images, but also affect the performance of vision algorithms, for a vision-guided robot.
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.
