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Here we develop a grammar model for person detection and show that it outperforms previous high-performance systems on the PASCAL benchmark. Our model ...
Missing: Recognition. | Show results with:Recognition.
In this paper we achieve new levels of performance for person detection using a grammar model that is richer than previous models used in high-performance ...
Missing: Recognition. | Show results with:Recognition.
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Abstract. In object recognition, the ability to share common parts or structure among related object classes allows information about parts.
Oct 23, 2008 · The framework uses probabilistic geometric grammars (PGGs) to represent object classes recursively in terms of their parts, thereby exploiting ...
A grammar model for person detection is developed and it outperforms previous high-performance systems on the PASCAL benchmark and introduces a new ...
Duration: 52:33
Posted: Aug 8, 2023
Posted: Aug 8, 2023
Missing: Grammatical | Show results with:Grammatical
PDF | We investigate the task of learning models for visual object recognition from natural language descriptions alone. The approach contributes to the.
Missing: Grammatical | Show results with:Grammatical
Abstract—This paper presents a framework for unsupervised learning of a hierarchical reconfigurable image template — the AND-OR.
The framework models the 3) geometric characteristics of object parts using multivariate conditional Gaussians over dimensions, position, and rotation. I ...
A novel proposal for object recognition based on relational grammars and Bayesian Networks is pre- sented. Based on a Symbol-Relation grammar an.