A Taxonomy of Part and Attribute Discovery Techniques

Chapter

Abstract

This chapter surveys recent techniques for discovering a set of Parts and Attributes (PnAs) in order to enable fine-grained visual discrimination between its instances. Part and Attribute (PnA)-based representations are popular in computer vision as they allow modeling of appearance in a compositional manner, and provide a basis for communication between a human and a machine for various interactive applications. Based on two main properties of these techniques a unified taxonomy of PnA discovery methods is presented. The first distinction between the techniques is whether the PnAs are semantically aligned, i.e., if they are human interpretable or not. In order to achieve the semantic alignment these techniques rely on additional supervision in the form of annotations. Techniques within this category can be further categorized based on if the annotations are language-based, such as nameable labels, or if they are language-free, such as relative similarity comparisons. After a brief introduction motivating the need for PnA based representations, the bulk of the chapter will be dedicated to techniques for PnA discovery categorized into non-semantic, semantic language-based, and semantic language-free methods. Throughout the chapter we will illustrate the trade-offs among various approaches though examples from the existing literature.

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Authors and Affiliations

  1. 1.University of MassachusettsAmherstUSA

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