Learning the Compositional Nature of Visual Objects

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Taming Transformers for High-Resolution Image Synthesis (2020, methodology): “Our key insight to obtain an effective and expressive model is that, taken together, convolutional and transformer architectures can model the compositional nature of our visual world [44]: We use a convolutional approach to efficiently learn a codebook of context-rich visual parts and, subsequently”
50 Years of object recognition: Directions forward (2013, background): “For example, Ommer et al. [183] and Ommer and Buhmann [184] propose a characteristic methodology that attempts to deal with the complexity of real world image categorization, by first performing a perceptual bottom-up grouping of edges followed by a top-down recursive grouping of features.”
Fast PRISM: Branch and Bound Hough Transform for Object Class Detection (2011, methodology): “These two techniques are commonly used and, as acknowledged by many authors (Leibe et al. 2008; Lazebnik et al. 2006; Ommer and Buhmann 2007; Philbin et al. 2008; Ferrari et al. 2007; Grauman and Darrell 2005), improve detection quality.”
Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? (2013, background): “…Ettinger [42], Geman et al. [59], [60], Mel and Fiser [114], Amit [1], [2], Hawkins [69], Fidler et al. [45], Scalzo and Piater [152], Ullman and Epshtein [191], DiCarlo and Cox [31], Ommer and Buhmann [126], Serre and Poggio [156], Pugeault et al. [207] and Rodríguez-Sánchez and Tsotsos [143].”