# Open-Vocabulary Object Detection via Neighboring Region Attention Alignment

Source: [Open-Vocabulary Object Detection via Neighboring Region Attention Alignment](<https://www.sota2.com/research/paper/68fa73351e209131cea5b295>)

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

Data updated: 2025-10-26T02:44:18.752Z

Authors: Sunyuan Qiang, Xianfei Li, Yanyan Liang, Wenlong Liao, Tao He, Pai Peng

Published: 2024-05-14T13:35:39.000Z

## Abstract

The nature of diversity in real-world environments necessitates neural network models to expand from closed category settings to accommodate novel emerging categories. In this paper, we study the open-vocabulary object detection (OVD), which facilitates the detection of novel object classes under the supervision of only base annotations and open-vocabulary knowledge. However, we find that the inadequacy of neighboring relationships between regions during the alignment process inevitably constrains the performance on recent distillation-based OVD strategies. To this end, we propose Neighboring Region Attention Alignment (NRAA), which performs alignment within the attention mechanism of a set of neighboring regions to boost the open-vocabulary inference. Specifically, for a given proposal region, we randomly explore the neighboring boxes and conduct our proposed neighboring region attention (NRA) mechanism to extract relationship information. Then, this interaction information is seamlessly provided into the distillation procedure to assist the alignment between the detector and the pre-trained vision-language models (VLMs). Extensive experiments validate that our proposed model exhibits superior performance on open-vocabulary benchmarks.

## 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.

- [Object Detection on LVIS](<https://www.sota2.com/research/sota/object-detection-on-lvis.md>): Object Detection; LVIS; APr: 21.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.08593>)

[PDF](<https://arxiv.org/pdf/2405.08593.pdf>)