From Visual Attributes to Adjectives through Decompositional Distributional Semantics

TL;DR

This paper presents a zero-shot learning approach that leverages linguistic and visual cues to automatically annotate images with attributes and objects, improving image understanding without requiring manual attribute labels.

Contribution

It introduces a method that decomposes visual representations into adjective-noun pairs, enabling attribute annotation without training data for attributes, and enhances object recognition performance.

Findings

Performs comparably to manual attribute annotation methods.

Outperforms other methods in attribute and object annotation.

Automatically constructs attribute-centric representations that improve object recognition.

Abstract

As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown...) attracting most attention. By building on the recent "zero-shot learning" approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as "visual phrases", and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it outperforms various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.