# Structured Visual Search via Composition-aware Learning

Source: [Structured Visual Search via Composition-aware Learning](<https://www.sota2.com/research/paper/68fa689d1e209131cea1d124>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:04:44.961Z

Authors: Mert Kilickaya, Arnold W.M. Smeulders

Published: 2020-10-27T16:52:03.000Z

## Abstract

This paper studies visual search using structured queries. The structure is in the form of a 2D composition that encodes the position and the category of the objects. The transformation of the position and the category of the objects leads to a continuous-valued relationship between visual compositions, which carries highly beneficial information, although not leveraged by previous techniques. To that end, in this work, our goal is to leverage these continuous relationships by using the notion of symmetry in equivariance. Our model output is trained to change symmetrically with respect to the input transformations, leading to a sensitive feature space. Doing so leads to a highly efficient search technique, as our approach learns from fewer data using a smaller feature space. Experiments on two large-scale benchmarks of MS-COCO and HICO-DET demonstrates that our approach leads to a considerable gain in the performance against competing techniques.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.14438>)

[PDF](<https://arxiv.org/pdf/2010.14438.pdf>)