A Problem Reduction Approach for Visual Relationships Detection

TL;DR

This paper introduces a reduction-based method for visual relationship detection, transforming the problem into object detection tasks, demonstrated successfully in a competitive challenge and achieving prize-winning results.

Contribution

It proposes a novel approach that reduces visual relationship detection to object detection, simplifying the problem and improving performance.

Findings

Achieved prize-winning performance in the ECCV 2018 challenge

Effectively detected object pairs with specific relationships

Validated the reduction approach on a large-scale dataset

Abstract

Identifying different objects (man and cup) is an important problem on its own, but identifying the relationship between them (holding) is critical for many real world use cases. This paper describes an approach to reduce a visual relationship detection problem to object detection problems. The method was applied to Google AI Open Images V4 Visual Relationship Track Challenge, which was held in conjunction with 2018 European Conference on Computer Vision (ECCV 2018) and it finished as a prize winner. The challenge was to build an algorithm that detects pairs of objects in particular relations: things like "woman playing guitar," "beer on table," or "dog inside car.".