Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features

TL;DR

This paper introduces a scalable object-centric image retrieval method that aggregates dense CLIP embeddings, significantly improving accuracy over global feature approaches and enabling efficient large-scale retrieval.

Contribution

The authors propose a novel aggregation of dense CLIP embeddings for object-centric retrieval, balancing scalability and detailed object identification.

Findings

Achieves up to 15 mAP points improvement over global features.

Effectively combines scalability with object-level retrieval capabilities.

Demonstrates advantages in large-scale retrieval frameworks.

Abstract

The task of open-vocabulary object-centric image retrieval involves the retrieval of images containing a specified object of interest, delineated by an open-set text query. As working on large image datasets becomes standard, solving this task efficiently has gained significant practical importance. Applications include targeted performance analysis of retrieved images using ad-hoc queries and hard example mining during training. Recent advancements in contrastive-based open vocabulary systems have yielded remarkable breakthroughs, facilitating large-scale open vocabulary image retrieval. However, these approaches use a single global embedding per image, thereby constraining the system's ability to retrieve images containing relatively small object instances. Alternatively, incorporating local embeddings from detection pipelines faces scalability challenges, making it unsuitable for retrieval from large databases. In this work, we present a simple yet effective approach to object-centric open-vocabulary image retrieval. Our approach aggregates dense embeddings extracted from CLIP into a compact representation, essentially combining the scalability of image retrieval pipelines with the object identification capabilities of dense detection methods. We show the effectiveness of our scheme to the task by achieving significantly better results than global feature approaches on three datasets, increasing accuracy by up to 15 mAP points. We further integrate our scheme into a large scale retrieval framework and demonstrate our method's advantages in terms of scalability and interpretability.