Efficient and robust analysis-by-synthesis in vision : A computational framework , behavioral tests , and modeling neuronal representations

By Ilker Yildirim. 2015

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Citations in the corpus

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation (2016, positive): “An example for face recognition is recent work [60] which combines a feedforward network like ours—also showing mirror-symmetric tuning of cell populations—with a probabilistic generative model.”
Automatic differentiation in machine learning: a survey (2015, background): “Besides deep learning, an interesting area where AD can be applied to computer vision problems is inverse graphics (Horn, 1977; Hinton and Ghahramani, 1997)—or analysis-bysynthesis (Yildirim et al., 2015)—where vision is seen as the inference of parameters for a generative model of a scene.”
3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare (2018, methodology): “using separately-trained models for the bottom-up and inference stages [25, 60, 34, 51], we employ a single unified end-to-end trained network for inverse-graphics.”
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views (2021, background): “To avoid the additional computational complexities of factorising or segmenting the objects in a scene into explicit multi-object representations, many works have used pre-segmented images [26, 27].”
PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition (2016, methodology): “The use of procedural graphics for generative models was further developed by Yildirim et al. [14], Kulkarni et al. [15], and Wu et al. [16].”