Learning to recognize objects

By Dan Roth, Ming-Hsuan Yang, Narendra Ahuja. 2000

185 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

ALICE: Active Learning with Contrastive Natural Language Explanations (2020, background): “Psychologists and philosophers have long posited natural language explanations as central, organizing elements to human learning and reasoning (Chin-Parker and Cantelon, 2017; Lombrozo, 2006; Smith, 2003).”
The Use of Classifiers in Sequential Inference (2001, neutral): “SNoW has already been used successfully for a variety of tasks in natural language and visual processing [10, 25].”
Young Children's Self-Generated Object Views and Object Recognition (2014, methodology): “Using a name comprehension task, Smith (2003) examined 18-and 24-month-old children’s ability to recognize 3-dimensional object caricatures like those in Figure 1 and richly detailed instances of the same categories.”
A Developmental Approach to Machine Learning? (2017, background): “…the shape bias co-develops not just with children’s learning of object names but also with object manipulation (Smith, 2005; James et al., 2014a), and with children’s emerging ability to recognize objects from abstract representations of 3-dimensional shape (Smith, 2003, 2013; Yee et al., 2012).”
Baby steps towards few-shot learning with multiple semantics (2019, background): “For example, it has been shown in the child development literature that infants’ object recognition ability is linked to their language skills and it is hypothesized that it might be related to the ability to describe objects [30].”
Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier] (2010, background): “Meaning is often inferred from events or observations that are received closely in time [5] [6].”
Learning a Sparse Representation for Object Detection (2002, background): “For example, in [8, 9 ], the object parts are local groupings of common primitive features such as edge fragments, while in [10], rectangle features are used to capture the presence of edges, bars and other simple structures.”