Foveation
8 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Towards Two-Stream Foveation-based Active Vision Learning
Specifically, the proposed framework models the following mechanisms: 1) ventral (what) stream focusing on the input regions perceived by the fovea part of an eye (foveation), 2) dorsal (where) stream providing visual guidance, and 3) iterative processing of the two streams to calibrate visual focus and process the sequence of focused image patches.
Noise-based Enhancement for Foveated Rendering
Novel image synthesis techniques, so-called foveated rendering, exploit this observation and reduce the spatial resolution of synthesized images for the periphery, avoiding the synthesis of high-spatial-frequency details that are costly to generate but not perceived by a viewer.
Foveation-based Deep Video Compression without Motion Search
In our learning based approach, we implement foveation by introducing a Foveation Generator Unit (FGU) that generates foveation masks which direct the allocation of bits, significantly increasing compression efficiency while making it possible to retain an impression of little to no additional visual loss given an appropriate viewing geometry.
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN
The FTT module was added to a VGG-11 CNN architecture and ten random initializations were trained on 20-class subsets of the Places and EcoSet datasets for scene and object classification respectively.
Convolutional Networks are Inherently Foveated
When convolutional layers apply no padding, central pixels have more ways to contribute to the convolution than peripheral pixels.
Evaluating Foveated Video Quality Using Entropic Differencing
To reduce stresses on bandwidth, foveated video compression is regaining popularity, whereby the space-variant spatial resolution of the retina is exploited.
The Foes of Neural Network’s Data Efficiency Among Unnecessary Input Dimensions
In this paper, we investigate the impact of unnecessary input dimensions on one of the central issues of machine learning: the number of training examples needed to achieve high generalization performance, which we refer to as the network's data efficiency.
Foveated Downsampling Techniques
Foveation is an important part of human vision, and a number of deep networks have also used foveation.
A Closed-Form Learned Pooling for Deep Classification Networks
This operator can learn a strict super-set of what can be learned by average pooling or convolutions.
Toward Standardized Classification of Foveated Displays
A display that moves its content along with the eye may be called a Foveated Display, though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity.