The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which has higher acuity at the center of gaze and a texture-like encoding mechanism in the periphery. To do so, we introduce models consisting of a first-stage fixed image transform followed by a second-stage learnable convolutional neural network, and we varied the first stage component. The primary model has a foveated-textural input stage, which we compare to a model with foveated-blurred input and a model with spatially-uniform blurred input (both matched for perceptual compression), and a final reference model with minimal input-based compression. We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models. Finally, both the foveated systems, either texture or blur-based, show a stronger center image-bias relative to the spatially-uniform systems . These results demonstrate that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides  computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system .
The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which has higher acuity at the center of gaze and texture-like encoding in the periphery. To do so, we introduce models consisting of a first-stage fixed image transform followed by a second-stage learnable convolutional neural network, and we varied the first stage component. The primary model has a foveated-textural input stage, which we compare to a model with foveated-blurred input and a model with spatially-uniform blurred input (both matched for perceptual compression), and a final reference model with minimal input-based compression. We find that : 1) the foveated-texture model shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models ; 2) the foveated-texture model has greater sensitivity to high-spatial frequency information and greater robustness to occlusion , w.r.t the comparison models; 3) both the foveated systems, show a stronger center image-bias relative to the spatially-uniform systems even with a weight sharing constraint. Critically, these results are preserved over different classical CNN architectures throughout their learning dynamics. Altogether, this suggests that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides symbiotic computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system , while also potentially inspiring the next generation of computer vision models via spatially-adaptive computation. Code + Data available here: URL
D
a
D
mechanism
R
the model with foveated texture-based input -- most akin to the human visual system --
: 1) the foveated-texture model
R
. Second,
; 2)
R
maintains
has greater
R
properties, similar to the reference, and unlike the comparison models. Finally,
, w.r.t the comparison models; 3)
D
either texture or blur-based,
R
. These results demonstrate
even with a weight sharing constraint. Critically, these results are preserved over different classical CNN architectures throughout their learning dynamics. Altogether, this suggests
A
symbiotic
R
.
, while also potentially inspiring the next generation of computer vision models via spatially-adaptive computation. Code + Data available here: URL