computer-vision-9

Foveation

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

DRAW: A Recurrent Neural Network For Image Generation

Daan Wierstra, Alex Graves, Danilo Jimenez Rezende, Ivo Danihelka, Karol Gregor•Sun Feb 15 2015

The Deep Recurrent Attentive Writer neural network architecture for image generation substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

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Emergent Properties of Foveated Perceptual Systems

Arturo Deza, Talia Konkle•Sat Jun 13 2020

Evidence is found that foveated perceptual systems learn a visual representation that is distinct from non-foveation perceptual systems, with implications in generalization, robustness, and perceptual sensitivity, which provides computational support for the idea that the foveate nature of the human visual system might confer a functional advantage for scene representation.

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Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

D. Tao, Lin Wu, Yang Wang, Junbin Gao•Sun Apr 29 2018

The Deep Co-attention based Comparators (DCCs) that fuse the co-dependent representations of the paired images so as to focus on the relevant parts of both images and produce their relative representations are introduced.

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Foveation for Segmentation of Ultra-High Resolution Images

D. Alexander, Ryutaro Tanno, Chen Jin, Moucheng Xu, T. Mertzanidou•Tue Jul 28 2020

Foveation module is introduced, a learnable "dataloader" which, for a given ultra-high resolution image, adaptively chooses the appropriate configuration (FoV/resolution trade-off) of the input patch to feed to the downstream segmentation model at each spatial location of the image.

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CUDA-Optimized real-time rendering of a Foveated Visual System

T. Poggio, Elian Malkin, Arturo Deza•Mon Dec 14 2020

This paper demonstrates how a GPU, with a CUDA block-wise architecture, can be employed for radially-variant rendering, with opportunities for more complex post-processing to ensure a metameric foveation scheme.

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Foveation in the Era of Deep Learning

George Killick, Paul Henderson, Paul Siebert, Gerardo Aragon-Camarasa•Sat Dec 02 2023

An end-to-end differentiable foveated active vision architecture that leverages a graph convolutional network to process foveation images, and a simple yet effective formulation for foveate image sampling is introduced.

5 0

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