Neural Comput - Learning intermediate-level representations of form and motion from natural movies.

Tópicos

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Resumo

We present a model of intermediate-level visual representation that is based on learning invariances from movies of the natural environment. The model is composed of two stages of processing: an early feature representation layer and a second layer in which invariances are explicitly represented. Invariances are learned as the result of factoring apart the temporally stable and dynamic components embedded in the early feature representation. The structure contained in these components is made explicit in the activities of second-layer units that capture invariances in both form and motion. When trained on natural movies, the first layer produces a factorization, or separation, of image content into a temporally persistent part representing local edge structure and a dynamic part representing local motion structure, consistent with known response properties in early visual cortex (area V1). This factorization linearizes statistical dependencies among the first-layer units, making them learnable by the second layer. The second-layer units are split into two populations according to the factorization in the first layer. The form-selective units receive their input from the temporally persistent part (local edge structure) and after training result in a diverse set of higher-order shape features consisting of extended contours, multiscale edges, textures, and texture boundaries. The motion-selective units receive their input from the dynamic part (local motion structure) and after training result in a representation of image translation over different spatial scales and directions, in addition to more complex deformations. These representations provide a rich description of dynamic natural images and testable hypotheses regarding intermediate-level representation in visual cortex.

Resumo Limpo

present model intermediatelevel visual represent base learn invari movi natur environ model compos two stage process earli featur represent layer second layer invari explicit repres invari learn result factor apart tempor stabl dynam compon embed earli featur represent structur contain compon made explicit activ secondlay unit captur invari form motion train natur movi first layer produc factor separ imag content tempor persist part repres local edg structur dynam part repres local motion structur consist known respons properti earli visual cortex area v factor linear statist depend among firstlay unit make learnabl second layer secondlay unit split two popul accord factor first layer formselect unit receiv input tempor persist part local edg structur train result divers set higherord shape featur consist extend contour multiscal edg textur textur boundari motionselect unit receiv input dynam part local motion structur train result represent imag translat differ spatial scale direct addit complex deform represent provid rich descript dynam natur imag testabl hypothes regard intermediatelevel represent visual cortex

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